docs: clarify PYTORCH_ENABLE_MPS_FALLBACK FAQ entry

Add basic conversion integration test
Restructure tests directory
2025-04-01 23:21:57 +02:00 · 2024-06-28 15:52:54 +02:00 · 2024-06-28 15:52:54 +02:00 · 2024-06-28 15:52:54 +02:00 · 2024-06-28 15:52:54 +02:00 · 2024-06-28 15:52:54 +02:00
45 changed files with 2011 additions and 4684 deletions
@@ -1,25 +0,0 @@
 name: Publish to Comfy registry
 on:
  workflow_dispatch:
  push:
    branches:
      - main
    paths:
      - "pyproject.toml"
 permissions:
  issues: write
 jobs:
  publish-node:
    name: Publish Custom Node to registry
    runs-on: ubuntu-latest
    if: ${{ github.repository_owner == 'aszc-dev' }}
    steps:
      - name: Check out code
        uses: actions/checkout@v4
      - name: Publish Custom Node
        uses: Comfy-Org/publish-node-action@v1
        with:
          ## Add your own personal access token to your Github Repository secrets and reference it here.
          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}
@@ -1,27 +0,0 @@
 name: Tier 0 — Unit (Linux)
 on:
  push:
    branches: [main]
  pull_request:
 # Deps are resolved from pyproject.toml via uv, so the toolchain pins live in
 # one place. Tier 0 must run without ComfyUI; the in-tree purity gate
 # (tests/unit/test_tier0_purity.py) enforces that the suite hasn't started
 # leaking framework imports.
 jobs:
  unit:
    runs-on: ubuntu-latest
    timeout-minutes: 10
    steps:
      - uses: actions/checkout@v4
      - uses: astral-sh/setup-uv@v7
        with:
          enable-cache: true
      - name: uv sync
        run: uv sync --no-install-project
      - name: Run Tier 0
        run: uv run pytest -m unit tests/ -v
@@ -1,134 +0,0 @@
 name: Tier 2 — M2 / ANE (self-hosted)
 on:
  pull_request:
    # `labeled` fires when run-m2 is first added; `synchronize`/`reopened`
    # re-run on every subsequent push while the label is present, so the
    # result tracks the PR head instead of going stale. The `if` below keeps
    # the run gated on the run-m2 label for all pull_request events.
    types: [labeled, synchronize, reopened]
  schedule:
    # Nightly at 04:00 UTC (~05/06 in PL). Keeps the M2 path honest
    # without burning the runner on every PR.
    - cron: "0 4 * * *"
  workflow_dispatch:
 jobs:
  m2:
    if: |
      github.event_name == 'schedule' ||
      github.event_name == 'workflow_dispatch' ||
      (github.event_name == 'pull_request' &&
       contains(github.event.pull_request.labels.*.name, 'run-m2'))
    # Self-hosted Apple Silicon runner. Prerequisites: COMFY_DIR pointing at
    # a runner-owned ComfyUI clone, plus a cached SD1.5 checkpoint.
    runs-on: [self-hosted, macOS, ARM64, coreml]
    timeout-minutes: 90
    steps:
      - uses: actions/checkout@v4
      # Hybrid ComfyUI strategy:
      #   - schedule (nightly)  -> latest origin/master + ComfyUI's own
      #     requirements.txt (constrained). Canary for upstream API breakage.
      #   - PR label / dispatch -> the requires-comfyui version tag + the frozen
      #     `comfy` uv group. Reproducible merge gate, immune to overnight drift.
      - name: Resolve ComfyUI ref + mode
        run: |
          if [ "$GITHUB_EVENT_NAME" = "schedule" ]; then
            echo "COMFY_MODE=latest" >> "$GITHUB_ENV"
            echo "COMFY_REF=master"  >> "$GITHUB_ENV"
          else
            # requires-comfyui is a semver constraint (e.g. ">=0.3.27"); pin the
            # gate to the matching ComfyUI release tag (vX.Y.Z).
            VERSION="$(sed -nE 's/^requires-comfyui *= *"[^0-9]*([0-9]+\.[0-9]+\.[0-9]+).*/\1/p' pyproject.toml)"
            if [ -z "$VERSION" ]; then echo "could not parse requires-comfyui from pyproject.toml"; exit 1; fi
            echo "COMFY_MODE=pinned"      >> "$GITHUB_ENV"
            echo "COMFY_REF=v$VERSION"    >> "$GITHUB_ENV"
          fi
      - name: Set up ComfyUI checkout
        # COMFY_DIR is exported by the self-hosted runner's .env and MUST be a
        # runner-owned ComfyUI clone (never your dev checkout — this step does
        # git reset --hard and rewrites custom_nodes). Cloned on first run.
        run: |
          set -euo pipefail
          if [ -z "${COMFY_DIR:-}" ]; then echo "COMFY_DIR unset"; exit 1; fi
          # Init-in-place rather than `git clone`: COMFY_DIR may already hold the
          # cached checkpoint (models/checkpoints) or converted .mlmodelc, and
          # `git clone` refuses a non-empty target. init + fetch + `checkout -f`
          # populates the ComfyUI tree while leaving untracked files (the
          # checkpoint, the cached models) untouched — so setup order is free.
          if [ ! -d "$COMFY_DIR/.git" ]; then
            echo "initialising ComfyUI repo in $COMFY_DIR"
            mkdir -p "$COMFY_DIR"
            git -C "$COMFY_DIR" init -q
          fi
          git -C "$COMFY_DIR" remote get-url origin >/dev/null 2>&1 \
            || git -C "$COMFY_DIR" remote add origin https://github.com/comfyanonymous/ComfyUI.git
          git -C "$COMFY_DIR" fetch --quiet origin
          if [ "$COMFY_MODE" = "latest" ]; then
            git -C "$COMFY_DIR" checkout -f -B master origin/master
          else
            git -C "$COMFY_DIR" checkout -f "$COMFY_REF"
          fi
          COMFY_SHA="$(git -C "$COMFY_DIR" rev-parse HEAD)"
          echo "COMFY_SHA=$COMFY_SHA" >> "$GITHUB_ENV"
          echo "Tier 2 mode=$COMFY_MODE, ComfyUI \`$COMFY_SHA\`" >> "$GITHUB_STEP_SUMMARY"
          # Point ComfyUI's custom-node loader at this checkout. Refresh the
          # symlink only; refuse to clobber a real directory (guards against a
          # COMFY_DIR that is accidentally a dev checkout).
          NODE_LINK="$COMFY_DIR/custom_nodes/ComfyUI-CoreMLSuite"
          if [ -e "$NODE_LINK" ] && [ ! -L "$NODE_LINK" ]; then
            echo "ERROR: $NODE_LINK is a real directory, not a symlink."
            echo "COMFY_DIR must be a runner-owned ComfyUI, not your dev checkout."
            exit 1
          fi
          mkdir -p "$COMFY_DIR/custom_nodes"
          ln -sfn "$GITHUB_WORKSPACE" "$NODE_LINK"
      - name: Install dependencies
        run: |
          set -euo pipefail
          if [ "$COMFY_MODE" = "latest" ]; then
            # Node deps (our coremltools-9 toolchain), then ComfyUI's own
            # requirements for the pulled SHA, capped by the toolchain ceiling.
            uv sync
            uv pip install -r "$COMFY_DIR/requirements.txt" \
              -c constraints/comfy-ceiling.txt
          else
            # Pinned gate: the frozen group mirrors the known-good pinned SHA.
            uv sync --group comfy
          fi
      - name: Start ComfyUI server (background)
        run: |
          cd "$COMFY_DIR"
          nohup "$GITHUB_WORKSPACE/.venv/bin/python" main.py --port 8188 --cpu-vae > /tmp/comfyui-ci.log 2>&1 &
          # Poll the HTTP endpoint for readiness — robust to startup-banner
          # wording / colored-log changes in a floating-latest ComfyUI.
          for _ in $(seq 1 90); do
            if curl -sf -o /dev/null http://127.0.0.1:8188/system_stats; then
              echo "comfy ready (ComfyUI ${COMFY_SHA:-unknown})"; exit 0
            fi
            sleep 2
          done
          echo "comfy failed to start"; tail -100 /tmp/comfyui-ci.log; exit 1
      - name: Purge cached Core ML UNets (force fresh conversion)
        # The converter skips when a model of the same name already exists. That
        # cache key is conversion *parameters* only, not the conversion code or
        # toolchain — so a stale model would let a conversion regression pass.
        # Clear it so every Tier 2 run exercises the full convert -> compile ->
        # sample path end to end.
        run: |
          rm -rf "$COMFY_DIR"/models/unet/*.mlpackage "$COMFY_DIR"/models/unet/*.mlmodelc || true
      - name: Run Tier 2 (m2 marker)
        # Drives the Core ML Converter node, which converts the UNet from the
        # checkpoint on every run (cache purged above).
        run: uv run --no-sync pytest -m m2 tests/ -v
      - name: Stop ComfyUI server
        if: always()
        run: pkill -f "main.py.*8188" || true
@@ -1,6 +1,3 @@
 playground/
 experiments/
 __pycache__/
 models/
 .venv/
 test_results/
 .claude/
@@ -1 +0,0 @@
 3.12
@@ -1,618 +0,0 @@
 # ComfyUI-CoreMLSuite — Converter Extraction Spec for Claude Code
 > **Companion to `MODERNIZATION_SPEC.md`.** That spec hardens the repo and (Phase 3)
 > splits the *inference* math from the framework. **This** spec splits the *conversion*
 > path (`safetensors → CoreML`) out into a standalone, `comfy`-free, pip-installable
 > package that CoreMLSuite then depends on — and that other projects (incl. on-device
 > iOS tooling) can reuse.
 >
 > **Same discipline as the modernization spec:** safety-net first, behavior-preserving
 > until told otherwise, one phase = one branch = one PR, `STOP — VALIDATE` gate between
 > every phase, golden-latent as the regression anchor. `[M2]` = needs macOS/Apple Silicon;
 > `[M2-ANE]` = needs the Neural Engine. Everything else must run on plain Linux/CI.
 ---
 ## 0. How to work (read first — non-negotiable)
 1. **Behavior-preserving until Phase E6.** Phases E1–E5 must not change image output, node
   names, `INPUT_TYPES` field names, or `NODE_CLASS_MAPPINGS` keys. The node graph is the
   public contract; saved user-workflow JSON breaks if these change.
 2. **The conversion package produces an artifact and stops there.** Its job ends at a written
   `.mlpackage` / `.mlmodelc` on disk. It must NOT import `comfy`, `folder_paths`, or
   `comfy_extras`, and must NOT know ComfyUI's `models/unet` layout. Paths are *inputs*.
 3. **The runtime loader stays in the suite.** The loader is the **local** `coreml_suite.coreml_model.CoreMLModel`
   — a thin wrapper over `coremltools.models.MLModel` (NOT Apple's
   `python_coreml_stable_diffusion.coreml_model.CoreMLModel`, which is no longer used; see #58).
   It *runs* a compiled model in Python — a desktop/Python inference concern, not a conversion
   concern. It is NOT moved into the package. (On iOS the `.mlmodelc` is loaded natively; the
   package's output is the deliverable, not a Python runner.)
 4. **Decouple in-repo before splitting repos.** Phases E1–E4 create the package *inside this
   repo* and prove equivalence. The physical second-repo split is Phase E5, only after the
   golden latent is proven identical. Do not create a second repository before Gate E4 passes.
 5. **Reuse the existing regression anchor.** The golden latent / PSNR anchor from
   `MODERNIZATION_SPEC.md` Phase 2 is the cross-cutting proof for every gate here. If it is not
   yet captured, capture it first (it is a prerequisite for E2 onward).
 6. **No new runtime dependencies** without flagging in the gate report (name, why, license, size).
 7. **A failing gate means stop and report**, not work around into the next phase.
 8. **Tooling is `uv`, not bare `pip`/`venv`.** Every environment/install/lock step uses the
   project's `uv` toolchain: `uv venv`, `uv pip install`, `uv pip install -e .`, `uv lock`,
   `uv run pytest`, `uv export`/`uv pip freeze` for baselines. Where this spec says "fresh venv",
   read "`uv venv` + `uv pip install`". Reserve `uv pip` (not `pip`) inside that venv too.
 9. **The package is the single source of truth for *what is possible*; the node is a thin,
   discovery-driven frontend.** See the "Interface contract" pillar below — this is the
   maintainer's hard requirement and it overrides the earlier (now-rescinded) "freeze the
   dropdown list" instruction.
 ---
 ## Interface contract (the maintainer's hard requirement) — read before any phase
 Two coupled guarantees must hold once the package is split out:
 **(A) Updating the converter must NOT require updating CoreMLSuite.**
 This is satisfied by treating the package's public surface as a versioned contract:
 - `convert(...)` and `compile_model(...)` are **keyword-only with defaults** for everything
  past the genuinely-required positionals (`ckpt_path`, `model_version`, `out_path`). New
  capabilities are added as new keyword args with defaults, so an old Suite's call still
  validates against a newer package. **Never** reorder or rename existing parameters.
 - `compose_out_name` (the `.mlpackage` filename = the cache key) **moves into the package** and
  is versioned with it. The Suite must not carry its own copy; if the package changes the naming
  scheme that is a **major** bump (old cached artifacts stop resolving).
 **(B) CoreMLSuite must be able to list *new* conversion types WITHOUT a Suite code change or
 version bump.** Today the node hardcodes its dropdowns:
 ```python
 "model_version": ([ModelVersion.SD15.name, ModelVersion.SDXL.name],),  # hand-typed, also INCOMPLETE (no LCM / SDXL_REFINER)
 "attention_implementation": (list(ATTENTION_IMPLEMENTATIONS),),         # from coreml_suite.attention
 "quantize_nbits": (list(QUANT_NBITS_VALUES), {"default": "none"}),      # from coreml_suite.core.naming
 ```
 These are replaced by **runtime discovery calls into the package**, evaluated inside
 `INPUT_TYPES` (ComfyUI re-evaluates `INPUT_TYPES` on every plugin load):
 ```python
 import coreml_diffusion
 "model_version":            (coreml_diffusion.list_model_versions(),),
 "attention_implementation": (coreml_diffusion.list_attention_impls(),),
 "quantize_nbits":           (coreml_diffusion.list_quant_modes(), {"default": "none"}),
 ```
 Effect: `uv pip install -U coreml_diffusion` + ComfyUI restart surfaces any newly-added type in the old
 plugin's dropdown — **no Suite edit, no Suite version bump.** This is the requirement.
 **The cost, stated honestly (accept this trade-off explicitly at Gate E0):**
 - The Suite becomes a "dumb" frontend; the package is the sole authority on what conversions
  exist. The Suite can no longer guarantee its saved workflows are valid against *arbitrary*
  future package versions.
 - Therefore the package's discovery identifiers (`ModelVersion` values, attn-impl strings, quant
  modes) are an **ADDITIVE-ONLY contract**: the package may *add* identifiers freely (minor bump,
  no Suite change); **removing or renaming an identifier is a breaking change requiring a MAJOR
  bump and a migration note**, because a saved workflow JSON references these strings verbatim.
  Without this rule, "no version bump" silently becomes "randomly broken workflows."
 - `INPUT_TYPES` must **fail soft** when the package is missing/old: wrap the discovery calls so a
  missing `coreml_diffusion` (or an old one lacking a `list_*` function) yields a sane fallback list and a
  logged warning, instead of the node failing to register and disappearing from the menu.
 **Discovery API the package must expose (stable names):**
 ```python
 coreml_diffusion.list_model_versions() -> list[str]   # VERIFIED ones only, e.g. ["SD15","SDXL"] today (.name — see seam.md)
 coreml_diffusion.list_attention_impls() -> list[str]  # ["SPLIT_EINSUM","SPLIT_EINSUM_V2","ORIGINAL"]
 coreml_diffusion.list_quant_modes() -> list[str]      # ["none","8","6","4"]
 coreml_diffusion.CONTRACT_VERSION: str                # bump rules above; Suite may log/compare it
 ```
 These return the *display strings already used today*, so existing workflows keep validating.
 **Verification status is a PACKAGE property, not a node hardcode (maintainer's intent).**
 The Suite wants to expose *every model the converter can verifiably convert*. Today `lcm` and
 `sdxl_refiner` are absent from the converter node not because the Suite chooses to hide them, but
 because they lack a full golden/PSNR verification. So the gating lives in the package as a status:
 ```python
 from enum import Enum
 class Status(Enum):
    VERIFIED = "verified"          # has a golden anchor + passing [M2-ANE] check
    EXPERIMENTAL = "experimental"  # convertible but not yet anchored/verified
 # internal registry, single source of truth.
 # KEY by ModelVersion enum MEMBER so list_* can emit .name. Keying by the lowercase
 # .value string returns ["sd15",...], which the node reverses via ModelVersion[...] -> KeyError.
 _MODEL_STATUS = {ModelVersion.SD15: Status.VERIFIED, ModelVersion.SDXL: Status.VERIFIED,
                 ModelVersion.SDXL_REFINER: Status.EXPERIMENTAL, ModelVersion.LCM: Status.EXPERIMENTAL}
 def list_model_versions(include_experimental: bool = False) -> list[str]:
    return [v.name for v, s in _MODEL_STATUS.items()   # .name -> "SD15","SDXL"; node reverses with ModelVersion[...]
            if s is Status.VERIFIED or (include_experimental and s is Status.EXPERIMENTAL)]
 ```
 Consequence: **promoting a model to VERIFIED in the package expands the Suite's dropdown with no
 Suite change and no Suite bump** — exactly the requirement. The act of verification (E-LCM
 produces an LCM golden anchor; same later for refiner) is what flips the status. The Suite's
 converter node calls `list_model_versions()` (verified-only); a power-user/CLI path may pass
 `include_experimental=True`. Promotion VERIFIED-from-EXPERIMENTAL is additive (minor bump);
 demotion or removal is breaking (major bump + note).
 ---
 ## Naming & layout (chosen — frozen at Gate E0)
 **Distribution name (PyPI):** `coreml-diffusion`. **Import name (Python):** `coreml_diffusion`.
 (PyPI normalizes `-`/`_`; the distribution uses the hyphen, the importable module the underscore.)
 Availability checked: both `coreml-diffusion` and the near variants were free on PyPI at E0.
 **Why this name (the positioning it encodes):** the project's niche is *diffusion models on Apple
 Neural Engine via CoreML, inside ComfyUI and on-device* — **not** Stable Diffusion specifically.
 `sd*` was rejected because it falsely narrows scope to SD; `coreml-diffusion` keeps `coreml` on the
 front for discoverability while `diffusion` honestly states the scope (SD/SDXL/LCM today, Flux and
 other diffusion architectures later) **without** promising arbitrary non-diffusion torch models,
 whose tracing/shape/sample-input pipeline differs. The name must not be re-narrowed to SD in
 future docs. ANE is the *differentiator* (documented in the README), but `coreml` was chosen over
 `ane` in the name for search discoverability per maintainer decision.
 Target package layout (framework-free — zero `comfy` imports):
 ```
 coreml_diffusion/
  __init__.py        # public API surface (see "Public API" below)
  model_version.py   # ModelVersion enum — the SINGLE source of truth, no comfy
  attention.py       # ATTENTION_IMPLEMENTATIONS tuple (from coreml_suite/attention.py) + apply_attention_implementation
  pipeline.py        # get_pipeline (from_single_file), get_unet (cml UNet from ref unet)
  unet.py            # UNet2DConditionModelLCM (moved from coreml_suite/lcm/unet.py)
  inputs.py          # get_sample_input, lcm_inputs, sdxl_inputs,
                     #   get_encoder_hidden_states_shape, get_coreml_inputs, get_inputs_spec
  controlnet.py      # add_cnet_support (conversion-side residual SHAPE calc only)
  convert.py         # convert_unet, convert (orchestration), convert_to_coreml, load_coreml_model
  compile.py         # compile_coreml_model
  quantize.py        # (Phase E6 / MODERNIZATION Phase 6 lands here) palettization 4/6/8-bit
  cli.py             # console entry point: `coreml-diffusion convert ...`
  pyproject.toml     # standalone packaging (at E5)
 ```
 What stays in `coreml_suite/` (the ComfyUI side, thinned):
 - `nodes.py` — still owns **name-encoding** (`out_name` construction), path resolution via
  `folder_paths`, the node `INPUT_TYPES`/mappings, and wrapping the result in `CoreMLModel`.
 - `models.py`, `latents.py`, `controlnet.py` (inference parts), `lcm/utils.py`, `config.py`
  (inference config build) — untouched by this spec except the import-source of `ModelVersion`.
 ### Public API (the contract `coreml_diffusion` exposes)
 ```python
 from coreml_diffusion import ModelVersion, convert, compile_model, compose_out_name
 from coreml_diffusion import list_model_versions, list_attention_impls, list_quant_modes, CONTRACT_VERSION
 # Mirror the CURRENT converter.py signature, made keyword-only past the required positionals
 # and with paths/device injected (no folder_paths, no comfy.model_management):
 # convert(ckpt_path, model_version, out_path, *,
 #         batch_size=1, sample_size=(64, 64), controlnet_support=False,
 #         lora_weights=None, attn_impl=list_attention_impls()[0], config_path=None,
 #         quantize_nbits="none", device=None) -> None   # side effect: writes out_path
 #   (current convert() returns None and writes via convert_unet → coreml_unet.save; keep that,
 #    or change to `return out_path` as a deliberate, documented improvement — pick one at E0.)
 # compile_model(src_path, out_dir, final_name) -> str   # returns compiled .mlmodelc path
 ```
 Note: `convert` takes an **explicit `out_path`** — no `folder_paths`. `device` is injected
 (defaults to torch's default device). `compose_out_name` lives here (cache-key contract) and the
 node imports it from the package. The `list_*` discovery functions back the node's dropdowns.
 ---
 ## The import chains to cut (root cause inventory) — REVISED against current code
 > **State note (verified):** the code moved on since the original draft. Several chains are
 > already cut. Re-verify each line by `grep` before acting; do not assume the original draft.
 **Already done (verify, then skip):**
 - ✅ `converter.py` already imports `from coreml_suite.model_version import ModelVersion`, and
  `model_version.py` is **clean** (`from enum import Enum` only — zero comfy). The old
  "converter → config → comfy" chain is **already broken**. `config.py` still imports comfy, but
  it is **inference-side** (`get_model_config` via `supported_models_base`/`latent_formats`) —
  *not* on the conversion path. Do **not** treat `config.py` as a converter dependency.
 - ✅ `converter.py` now uses `diffusers.UNet2DConditionModel.from_single_file` and a local
  `CoreMLUNetWrapper` (in `coreml_suite/conversion/unet.py`) — it is **no longer** importing the
  Apple `python_coreml_stable_diffusion.unet.UNet2DConditionModel*` internals on the main path.
  A `coreml_suite/conversion/` subpackage already exists (`attention`, `shapes`, `trace`, `unet`).
 - ✅ Name-encoding already extracted to `coreml_suite/core/naming.py` (`compose_out_name`,
  `lora_names_from_params`, `ATTN_SUFFIX`, `QUANT_NBITS_VALUES`) **with characterization tests**
  (`tests/unit/test_characterization_out_name.py`). The pure-naming split is done.
 - ✅ Quantization is **already implemented** in `converter.py` (`quantize_nbits`, k-means
  `palettize_weights`) and surfaced as an optional node input. Phase E6 is therefore *move*, not
  *build* (see revised E6).
 **Still to cut (the real remaining work):**
 1. `coreml_suite/converter.py::get_out_path` → `from folder_paths import get_folder_paths`.
   Main converter still reaches into ComfyUI's model dir. **Cut: `out_path` is an injected arg;
   `folder_paths` resolution moves up into the node** (the node already computes `out_name`).
 2. `coreml_suite/lcm/converter.py` → still has its **own** `from folder_paths import
   get_folder_paths` (`get_out_path`) and (per original draft) `comfy.model_management`. Verify
   the current LCM file and cut both: inject `out_path` and `device`.
 3. Global mutation of the attention impl: confirm where it now lives. Main path appears to route
   through `coreml_suite/conversion/attention.apply_attention_implementation` (cleaner than the
   old global), but `lcm/converter.py` may still set a module global at import. **Ensure the
   package sets attention per-call, never at import time.**
 4. **Duplication LCM vs main:** `lcm/converter.py` still carries its own copies of
   `convert_to_coreml`, `load_coreml_model`, `get_out_path`, `get_sample_input` (the LCM variant
   takes a `scheduler` arg), and hardcodes `SimianLuo/LCM_Dreamshaper_v7`. **Dedupe into the
   single `coreml_diffusion` implementation;** the HF-hardcode consolidation is the *behavior-changing*
   part → deferred to optional **E-LCM**, not E1–E5.
 5. **`compose_out_name` ownership:** currently in `coreml_suite/core/naming.py` and called by the
   node. Per the Interface-contract pillar it must **move into the package** (it is the cache-key
   contract) and the node must import it from `coreml_diffusion`, not keep a copy.
 ---
 ## Phase E0 — Seam decision & inventory (no code change)
 **Objective:** lock the cut line, the interface contract, and naming so later phases don't drift.
 ### Tasks
 1. Produce `docs/extraction/seam.md`: a table of every symbol in `converter.py`,
   `lcm/converter.py`, `lcm/unet.py`, **plus the already-extracted `conversion/` subpackage
   (`attention`, `shapes`, `trace`, `unet`) and `core/naming.py`**, classified
   **CONVERSION → coreml_diffusion** vs **STAYS (comfy/node)**. Note which are already framework-free.
 2. ~~Confirm the current `python_coreml_stable_diffusion` footprint.~~ **DONE (seam.md §6):
   footprint is ZERO** — no runtime imports anywhere; only a docstring mention in
   `core/__init__.py:4`. Main path uses `diffusers` + local `CoreMLUNetWrapper`; the runtime
   `CoreMLModel` (STAYS in suite) is a local coremltools wrapper, not Apple's. No shape/attn helper
   comes from Apple (local `conversion/shapes.py`, `conversion/attention.py`).
 3. **Decide the interface contract concretely (the maintainer's hard requirement):**
   - Discovery functions `list_model_versions / list_attention_impls / list_quant_modes` live in
     the package and return today's display strings verbatim. Node `INPUT_TYPES` calls them.
   - `ModelVersion` values, attn-impl strings, quant modes are **ADDITIVE-ONLY** across package
     versions; removal/rename = MAJOR bump + migration note. Write this into the package's
     versioning policy doc now.
   - `compose_out_name` moves to the package; node imports it (no copy). Confirm the
     characterization tests in `test_characterization_out_name.py` will be re-pointed, not
     duplicated.
   - **Resolve the `model_version` dropdown question (maintainer decided):** the Suite exposes
     *every model the converter can verifiably convert*. `lcm` and `sdxl_refiner` are absent today
     only because they lack a golden/PSNR verification — **not** because the node hardcodes a
     short list. Encode this as a **status registry in the package** (`VERIFIED` vs
     `EXPERIMENTAL`); `list_model_versions()` returns VERIFIED-only by default. The converter node
     calls it plainly. Promoting LCM/refiner to VERIFIED (after E-LCM / a refiner anchor) expands
     the dropdown with **no Suite change**. Do NOT add permanent per-node filtering — the gate is
     verification status, owned by the package.
 4. ~~Confirm the `ml-stable-diffusion` git dep is pinned.~~ **N/A — already removed (#58).** Verified:
   zero `python_coreml_stable_diffusion` imports in the repo; `CoreMLModel` is now a local
   coremltools wrapper; the dep is absent from `pyproject.toml`/`requirements.txt`. No SHA to pin.
 ### STOP — VALIDATE (Gate E0)
 ```
 ## Gate E0 report
 - seam.md committed: <path>; symbol counts (move / stay / already-framework-free)
 - python_coreml_stable_diffusion usage (verified by grep): conversion=<list> runtime=<list>
 - Discovery API signatures frozen: list_model_versions (verified-only) / list_attention_impls / list_quant_modes
 - Status registry decided: sd15+sdxl=VERIFIED, lcm+sdxl_refiner=EXPERIMENTAL (gated, not hidden)
 - Additive-only contract policy doc written (incl. promotion=minor, demotion/removal=major): <path>
 - model_version dropdown: expose all (incl. LCM/REFINER) / filtered per node — DECISION: <...>
 - compose_out_name move-not-copy confirmed; tests re-point plan: <...>
 - LCM consolidation deferred to optional E-LCM: YES/NO
 - ml-stable-diffusion: N/A — already removed (#58), not a dependency (was: pin-or-BLOCKER)
 - Package name in-repo: coreml_diffusion (final PyPI name deferred to E5)
 ```
 ---
 ## Phase E1 — Establish `coreml_diffusion` package + discovery API (mostly verification)
 **Objective:** stand up the package namespace and the discovery surface. Much of the comfy-chain
 cut is **already done** — this phase mostly *verifies* that and adds the discovery functions.
 ### Tasks
 1. **Verify (don't redo):** `coreml_suite/model_version.py` is already clean (`Enum` only). Confirm
   `import coreml_suite.model_version` works with **no comfy** (`uv run python -c "..."` in a
   comfy-free `uv venv`). If true, E1's original "extract ModelVersion" task is already satisfied.
 2. Create the `coreml_diffusion/` package skeleton with `__init__.py` exporting the **discovery API**
   backed by the *existing* sources of truth for now (re-export `ModelVersion`, the
   `ATTENTION_IMPLEMENTATIONS` tuple, and `QUANT_NBITS_VALUES`) so values are byte-identical:
   ```python
   def list_model_versions(): return [v.name for v in ModelVersion]   # .name -> "SD15" (node reverses via ModelVersion[...]; .value KeyErrors)
   def list_attention_impls(): return list(ATTENTION_IMPLEMENTATIONS)
   def list_quant_modes(): return list(QUANT_NBITS_VALUES)
   CONTRACT_VERSION = "1.0"
   ```
   (At this stage `coreml_diffusion` may live inside the repo and import from `coreml_suite.*`; the
   physical move of implementation happens in E2. The point of E1 is to freeze the *contract*.)
 3. **Decided (`.name`):** the node renders `ModelVersion.SD15.name` (`"SD15"`) and reverses the
   dropdown string via `ModelVersion[model_version]` (name lookup, `nodes.py:286`). Discovery API
   therefore returns `.name`; `.value` (`"sd15"`) would `KeyError`. Recorded in `seam.md` §5.
 ### Acceptance criteria
 - `uv run python -c "import coreml_diffusion; print(coreml_diffusion.list_model_versions(), coreml_diffusion.list_quant_modes())"`
  works in a **comfy-free** `uv venv` and prints today's exact strings.
 - Existing characterization tests pass unchanged.
 - No node behavior change yet (node still uses its current hardcoded lists in E1).
 ### STOP — VALIDATE (Gate E1)
 ```
 ## Gate E1 report
 - model_version.py confirmed comfy-free (uv, no comfy): PASS/FAIL
 - coreml_diffusion.list_* returns byte-identical strings to current dropdowns: YES/NO (show values)
 - .name vs .value decision for model_version discovery: <...>
 - CONTRACT_VERSION set; additive-only policy linked: <path>
 - Characterization tests unchanged & green (uv run pytest): YES/NO
 ```
 ---
 ## Phase E2 — Move conversion code into `coreml_diffusion` (in-repo, dedup, behavior-preserving)
 **Objective:** physically relocate the conversion mechanics into the framework-free package,
 collapsing the two duplicate converters into one, with paths/device injected.
 ### Tasks
 1. Move into `coreml_diffusion/`: `pipeline.py` (`get_pipeline`, `get_unet`), `unet.py`
   (`UNet2DConditionModelLCM`), `inputs.py` (sample/lcm/sdxl input builders +
   `get_encoder_hidden_states_shape` + `get_coreml_inputs` + `get_inputs_spec`),
   `controlnet.py` (`add_cnet_support`), `convert.py` (`convert_unet`, `convert`,
   `convert_to_coreml`, `load_coreml_model`), `compile.py` (`compile_coreml_model`).
 2. **Dedupe LCM vs main** (the real remaining duplication): delete `lcm/converter.py`'s copies of
   `convert_to_coreml` / `load_coreml_model` / `get_out_path` / `get_sample_input` (LCM variant
   carries a `scheduler` arg — fold that into the shared `get_sample_input` as an optional param)
   in favor of the single `coreml_diffusion` implementation. The main path's helpers
   (`get_unet`/`get_encoder_hidden_states_shape`/`get_coreml_inputs`/`convert_unet`/`convert`) and
   the `conversion/` subpackage (`attention`, `shapes`, `trace`, `unet`) move as-is.
 3. **Inject paths**: replace `get_out_path`'s `folder_paths` reach-in with an injected `out_path`
   argument on `convert(...)`; `folder_paths` resolution moves up into the node (which already
   computes `out_name`). No `folder_paths` import anywhere in `coreml_diffusion`.
 4. **Inject device** where the LCM path used `comfy.model_management` (verify it still does):
   `convert(..., device=None)`, default to torch's default device.
 5. **Attention per-call, never at import:** main path already routes through
   `conversion/attention.apply_attention_implementation` — keep that. If `lcm/converter.py` still
   sets any module global at import, remove it; the package sets attention from the `attn_impl`
   arg inside `convert`.
 6. **Move `compose_out_name` into the package** (`coreml_diffusion/naming.py`); re-point
   `test_characterization_out_name.py` imports to `coreml_diffusion.naming` — assertions and values
   unchanged. The node will import it from the package in E3.
 7. Leave **thin shims** in `coreml_suite/converter.py` and `coreml_suite/lcm/converter.py` that
   re-export from `coreml_diffusion`, preserving the old call signatures the nodes use (nodes untouched
   this phase). Shims map comfy `folder_paths`/device into package args.
 ### Acceptance criteria
 - `uv run pytest -m unit` (Tier 0) imports `coreml_diffusion.*` with **no comfy / no MPS** and is green on Linux.
 - The dedup leaves exactly one implementation of each previously-duplicated function.
 - Characterization tests pass unchanged after the `compose_out_name` re-point.
 - `[M2]` A real SD1.5 conversion via the shim still produces a loadable model.
 - `[M2-ANE]` **Golden latent identical / within tolerance** to the MODERNIZATION Phase 2 anchor
  (same seed/prompt) — proves the move + dedup changed nothing.
 ### STOP — VALIDATE (Gate E2 — first regression gate)
 ```
 ## Gate E2 report
 - Tier 0 import of coreml_diffusion without comfy/MPS (uv run): PASS/FAIL
 - LCM/main duplicated funcs collapsed to one (list old→new): <map>
 - compose_out_name moved to package; char-tests re-pointed & green: YES/NO
 - Paths injected (no folder_paths in package): confirmed
 - Device injected (no comfy.model_management in package): confirmed
 - Attention set per-call, not at import (both main & lcm): confirmed
 - [M2-ANE] Golden latent vs Phase-2 anchor: identical / within tol <x> / DIVERGED (STOP)
 - Node INPUT_TYPES / mappings untouched: confirmed (diff)
 ```
 **If the golden latent diverged at all, STOP and report — do not continue.**
 ---
 ## Phase E3 — Thin the nodes onto the package (behavior-preserving)
 **Objective:** remove the shims; have the ComfyUI nodes call `coreml_diffusion` directly, keeping the
 node contract byte-identical.
 ### Tasks
 1. `CoreMLConverter.convert` (in `coreml_suite/nodes.py`): keep the `folder_paths`-based path
   resolution **in the node**; import `compose_out_name` from `coreml_diffusion` (not `coreml_suite.core`);
   call `coreml_diffusion.convert(...)` and `coreml_diffusion.compile_model(...)` directly; wrap the compiled path
   in `CoreMLModel`.
 2. **Wire the dropdowns to discovery (the maintainer's hard requirement).** Replace the hardcoded
   `INPUT_TYPES` lists with fail-soft discovery calls:
   ```python
   def _discover(fn, fallback):
       try:
           import coreml_diffusion
           return getattr(coreml_diffusion, fn)()
       except Exception as e:        # missing/old package, or import error
           logger.warning(f"coreml_diffusion.{fn} unavailable ({e}); using fallback {fallback}")
           return fallback
   ...
   "model_version":            (_discover("list_model_versions", ["SD15", "SDXL"]),),
   "attention_implementation": (_discover("list_attention_impls", ["SPLIT_EINSUM","SPLIT_EINSUM_V2","ORIGINAL"]),),
   "quantize_nbits":           (_discover("list_quant_modes", ["none","8","6","4"]), {"default": "none"}),
   ```
   This is what makes "update the package → new types appear in the old node, no Suite bump" true.
 3. `COREML_CONVERT_LCM` (in `coreml_suite/lcm/nodes.py`): route through `coreml_diffusion` for the shared
   mechanics. **Keep the existing LCM behavior/HF-hardcode for now** — consolidation is optional E-LCM.
 4. Delete the now-dead `coreml_suite/converter.py` / `coreml_suite/lcm/converter.py` shims (or
   reduce to a one-line re-export if anything external imports them — grep first).
 ### Acceptance criteria
 - `NODE_CLASS_MAPPINGS` / `NODE_DISPLAY_NAME_MAPPINGS` keys: **unchanged** (diff `__init__.py`).
 - Every `INPUT_TYPES` **field name** unchanged. Dropdown **values**: the discovery calls must
  return **a superset of** today's values, with every previously-present value still present and
  spelled identically (additive-only). *(This deliberately replaces the original spec's
  "values must be byte-identical/frozen" criterion — the maintainer requires the list be
  extensible at runtime. Frozen-field-names + additive-only-values is the new contract.)*
 - With `coreml_diffusion` **absent**, the node still registers and shows the fallback lists (fail-soft).
 - `[M2-ANE]` Golden latent still identical to the Phase-2 anchor.
 - `[M2-ANE]` The committed e2e workflow `tests/integration/...` still passes (PSNR > 25).
 ### STOP — VALIDATE (Gate E3)
 ```
 ## Gate E3 report
 - Node mappings diff: empty (confirmed)
 - INPUT_TYPES field-names diff: empty (confirmed)
 - Dropdown values: superset of prior, all prior values still present & identical: YES/NO (show)
 - Fail-soft with coreml_diffusion absent (node still registers): PASS/FAIL
 - compose_out_name now imported from coreml_diffusion (no node-side copy): confirmed
 - [M2-ANE] Golden latent vs anchor: identical / within tol / DIVERGED (STOP)
 - [M2-ANE] e2e workflow PSNR: <value> (> 25?)
 - Dead converter shims removed / reduced: <list>
 ```
 ---
 ## Phase E4 — Standalone packaging & CLI (still in-repo)
 **Objective:** make `coreml_diffusion` independently installable and usable without ComfyUI, with a CLI
 suitable for the planned article and for on-device/iOS conversion workflows.
 ### Tasks
 1. Add `coreml_diffusion/pyproject.toml`: name (working `coreml_diffusion`), `requires-python`, dependencies
   = `coremltools` (pinned to the MODERNIZATION-validated version), `diffusers`, `transformers`,
   `peft`, `omegaconf`, `numpy`, `torch`. **No `ml-stable-diffusion`** (already removed in #58, see
   §0.3) and **no comfy**. Suite pins `transformers>=4.44`/`peft>=0.13`/`omegaconf>=2.3` today;
   grep-confirm each is on the conversion path before listing it. A `[project.scripts]` entry:
   `coreml-diffusion = "coreml_diffusion.cli:main"`.
 2. `coreml_diffusion/cli.py`: `coreml-diffusion convert --ckpt PATH --model-version sd15 --out PATH
   [--height --width --batch-size --attn-impl --controlnet --lora NAME:STRENGTH ... --config PATH]`
   and `coreml-diffusion compile --src PATH --out-dir DIR --name NAME`. Mirrors `convert()`/`compile_model()`.
 3. Tier-0 Linux tests for the CLI **arg→call mapping** (mock the heavy `convert`); the real
   convert remains `[M2]`. Add a `[M2]` smoke test: convert a tiny synthetic UNet end-to-end.
 4. README for the package: install, CLI usage, "produce a `.mlpackage`/`.mlmodelc` for use in a
   Swift/iOS app", and the ANE positioning note (low-power, GPU-free, embeddable; SD1.5/SDXL on
   ANE, **not** a Flux-speed claim).
 ### Acceptance criteria
 - Fresh `python -m venv` + `uv pip install ./coreml-diffusion` (no ComfyUI present) imports and runs
  `coreml-diffusion --help` and the arg-mapping tests on Linux.
 - `[M2]` `coreml-diffusion convert` produces a model file identical (golden) to the node path.
 ### STOP — VALIDATE (Gate E4)
 ```
 ## Gate E4 report
 - uv pip install ./coreml-diffusion in comfy-free venv: PASS/FAIL (log)
 - CLI arg→call tests (Tier 0, Linux): green
 - [M2] CLI-produced model golden vs node-produced model: identical / DIVERGED
 - Package deps list (with pinned SHAs/versions + licenses):
 - New runtime deps vs suite before: <none / list>
 ```
 **This is the gate that proves the package stands alone. Do not split repos before it passes.**
 ---
 ## Phase E5 — Physical split into a second repository
 **Objective:** move `coreml_diffusion/` to its own repo; CoreMLSuite depends on it by pinned version.
 ### Tasks
 1. Create the new repo (maintainer action — agent prepares the tree, not the GitHub repo).
   Choose final distributable name; rename imports if changed (single sweep, recorded).
 2. CoreMLSuite `pyproject.toml` / `requirements.txt`: replace the conversion-only deps with a
   pinned dependency on the new package (`coreml_diffusion==<version>` from PyPI, or `git+...@<tag>`
   until first PyPI release). (There is no `git+...ml-stable-diffusion` line to remove — already
   gone since #58.)
 3. ~~Keep `python_coreml_stable_diffusion` for the loader.~~ **Void.** The loader is the local
   `coreml_suite/coreml_model.py` over `coremltools`; the suite keeps `coremltools` as a direct dep
   for it. No Apple lib involved.
 4. Set up the new repo's CI: Tier 0 on Linux (import + arg-mapping + input-shape math),
   `[M2]`/`[M2-ANE]` on a self-hosted/macOS-ARM runner reusing the golden-latent anchor.
 5. Versioning: SemVer; first release `0.1.0`. Document the compatibility matrix
   (coreml_diffusion ↔ coremltools version ↔ diffusers version). No ml-stable-diffusion axis.
 ### Acceptance criteria
 - CoreMLSuite installs in a fresh venv pulling the new package; e2e workflow still passes `[M2-ANE]`.
 - New repo CI green on Linux (Tier 0) and `[M2-ANE]` golden latent matches the anchor.
 - No conversion code remains in CoreMLSuite (grep: no `ct.convert`, no `from_single_file`,
  no `torch.jit.trace`).
 ### STOP — VALIDATE (Gate E5)
 ```
 ## Gate E5 report
 - New repo tree prepared: <path/branch>; final package name: <name>
 - Suite depends on package by pinned version: <spec>
 - Suite e2e [M2-ANE] PSNR after split: <value> (> 25?)
 - Conversion code fully absent from suite: confirmed (grep output)
 - Compatibility matrix documented: <link>
 - First release tag: 0.1.0
 ```
 ---
 ## Phase E6 — Quantization travels WITH the conversion code (already implemented → move)
 **Objective:** quantization is **already implemented** (k-means `palettize_weights` in
 `converter.py`, `quantize_nbits` node input, `_q<bits>` filename suffix, README tradeoff table).
 There is nothing to *build*. It simply **moves with the conversion code in E2** as part of
 `convert_unet`. This phase is a checkpoint that it survived the extraction intact, plus exposing
 it through the CLI.
 ### Tasks
 1. Confirm the palettization block moved cleanly into `coreml_diffusion` (lives in `convert.py` or a
   `quantize.py` helper called from `convert_unet`). Default `"none"` stays byte-identical.
 2. Expose via CLI flag `--quantize {none,8,6,4}` (E4 already lists this) and via
   `list_quant_modes()` discovery (E1/E3).
 3. The existing README tradeoff table (SD1.5 1×512×512 SPLIT_EINSUM: none/8/6/4 → size/ms/PSNR)
   moves to the package README. Re-confirm one row `[M2-ANE]` so the article can cite a live number.
 ### Acceptance criteria
 - Default (`none`) output byte-identical to pre-extraction (covered by the E2/E3 golden latent).
 - `coreml-diffusion convert --quantize 4` produces a `_q4` artifact matching the node's `_q4` artifact `[M2]`.
 - `list_quant_modes()` drives the node dropdown (no hardcoded copy remains).
 ### STOP — VALIDATE (Gate E6)
 ```
 ## Gate E6 report
 - Palettization relocated into coreml_diffusion, called from convert_unet: confirmed
 - Default none output identical (golden): YES/NO
 - [M2] CLI --quantize {8,6,4} artifacts match node artifacts: YES/NO
 - Tradeoff table in package README with at least one re-confirmed [M2-ANE] row: <link>
 ```
 ---
 ## Phase E-LCM — FIRST task after the split: clean up LCM + verify → promote (behavior-changing, gated)
 > Promoted from "optional, someday" to **the first thing after E5**, per maintainer intent: the
 > Suite should expose every verifiably-convertible model, and LCM is the obvious first cleanup.
 Two coupled goals:
 1. **Consolidate the LCM path.** Make the LCM node use the unified `from_single_file` path in
   `coreml_diffusion.convert(model_version=LCM, ...)` instead of the hardcoded `SimianLuo/LCM_Dreamshaper_v7`
   HF download; drop the duplicated LCM helpers (already deduped in E2). **Behavior change** ⇒
   capture an LCM golden anchor *before* the change, then prove within-tolerance after.
 2. **Verify → promote.** Once the LCM conversion has a passing `[M2-ANE]` golden anchor, flip
   `_MODEL_STATUS["lcm"] = Status.VERIFIED` **in the package** (minor bump). The Suite's dropdown
   gains `lcm` automatically — no Suite change, no Suite bump. This is the end-to-end proof that
   the discovery contract works as designed.
 Repeat the same recipe for `sdxl_refiner` when it gets an anchor (separate small gate). Do NOT
 bundle E-LCM into E1–E5; it changes behavior and must stand on its own golden.
 ### STOP — VALIDATE (Gate E-LCM)
 ```
 ## Gate E-LCM report
 - LCM golden anchor captured BEFORE change: <path/hash>
 - LCM node now uses unified from_single_file path; HF hardcode removed: confirmed
 - [M2-ANE] LCM golden after change: identical / within tol <x> / DIVERGED (STOP)
 - Status flipped lcm→VERIFIED in package (minor bump <ver>): confirmed
 - Suite dropdown now lists lcm with NO Suite code change / NO Suite bump: confirmed (diff empty)
 - LCM node accepts a checkpoint arg now (documented breaking-ish UI note): <link>
 ```
 ---
 ## Article deliverable (after E4)
 Once the CLI exists and stands alone, the "convert a Comfy/A1111 workflow into an on-device iOS
 app" write-up becomes a clean tutorial: `coreml-diffusion convert` → `.mlmodelc` → load in Swift/CoreML.
 Frame the niche honestly per the README note above (ANE feasibility & power, not raw Flux speed).
 ---
 ## Quick reference: extraction gate discipline
 ```
 E0  Seam decision, interface contract, discovery API frozen → Gate E0  (cut line + additive-only policy?)
 E1  Stand up coreml_diffusion + discovery API (mostly verify)        → Gate E1  (list_* byte-identical, comfy-free?)
 E2  Move conversion code, dedup LCM/main, inject paths/device→ Gate E2  (golden identical? duplicates gone?) ← first regression gate
 E3  Thin nodes onto package + wire discovery dropdowns       → Gate E3  (field-names frozen, values additive, fail-soft, golden identical?)
 E4  Standalone packaging + CLI (uv)                          → Gate E4  (uv pip install w/o comfy? CLI golden?) ← proves it stands alone
 E5  Physical second-repo split                               → Gate E5  (suite depends on pkg? conversion absent?)
 E-LCM  FIRST post-split: clean up LCM, verify → promote      → Gate E-LCM (LCM golden? dropdown gains lcm w/ no Suite bump?)
 E6  Quantization checkpoint (already built → moved in E2)    → Gate E6  (default identical? CLI quant matches?)
 (refiner)  same recipe as E-LCM when an anchor exists        → own small gate (promote sdxl_refiner→VERIFIED)
 ```
 **Interface-contract invariants (the maintainer's hard requirement), restated:**
 - Package API is keyword-only-with-defaults past the required positionals → converter updates
  don't force Suite updates.
 - Node dropdowns are discovery-driven (`coreml_diffusion.list_*`) + fail-soft → new conversion types
  appear in the old plugin with `uv pip install -U coreml_diffusion`, **no Suite code change, no bump**.
 - Discovery identifiers are **additive-only**; removal/rename = MAJOR bump + migration note.
 - `compose_out_name` (cache key) lives in the package, single copy.
 **Golden rule (inherited): never cross a gate with a failing acceptance criterion.
 Stop, report, wait. The golden latent is the single source of truth that the extraction
 changed nothing.**
@@ -1,21 +1,674 @@
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@@ -8,8 +8,8 @@ These models are designed to leverage the Apple Neural Engine (ANE) on Apple Sil
 thereby enhancing your workflows and improving performance.
 If you're not sure how to obtain these models, you can download them
-[here](https://huggingface.co/coreml-community) or convert your own checkpoints
+[here](https://huggingface.co/coreml-community) or convert your own models using
-directly with the conversion nodes in this suite (see [How to use](#how-to-use)).
+[coremltools](https://github.com/apple/ml-stable-diffusion).
 In simple terms, think of Core ML models as a tool that can help your ComfyUI work faster and more efficiently.
 For instance, during my tests on an M2 Pro 32GB machine,
@@ -81,29 +81,6 @@ These custom nodes come with a host of features, including:
 > [!NOTE]  
 > This repository will continue to be updated with more nodes and features over time.
 ## Conversion & Acknowledgements
 The Core ML conversion pipeline in this repository began as an adaptation of
 Apple's [ml-stable-diffusion](https://github.com/apple/ml-stable-diffusion),
 which pioneered running Stable Diffusion on the Apple Neural Engine. The
 implementation has since diverged and no longer depends on that package:
 - UNet conversion runs natively on `diffusers`' `UNet2DConditionModel`.
 - The ANE-friendly attention path (`SPLIT_EINSUM`, `SPLIT_EINSUM_V2`) is
  reimplemented as standalone `diffusers` attention processors.
 - The toolchain tracks current ComfyUI (NumPy 2, Torch 2.7, coremltools 9,
  Python 3.12).
 The goal is to keep iterating on these methods independently and to explore
 support beyond SD1.5.
 > [!IMPORTANT]
 > **Breaking change in 2.0.0.** The converted Core ML UNet now takes
 > `encoder_hidden_states` in the native `diffusers` layout
 > `(batch, tokens, hidden)` instead of the previous
 > `(batch, hidden, 1, tokens)`. Core ML models converted with earlier versions
 > are not compatible with 2.0.0 and must be re-converted.
 ## Installation
 ### Using ComfyUI-Manager
@@ -193,8 +170,8 @@ the node name, so if the model already exists, the node will not convert it agai
    - **ckpt_name**: The name of the checkpoint to convert. This should be the name of the checkpoint file stored in the
      `models/checkpoints` directory.
    - **model_version**: Whether the model is based on SD1.5 or SDXL.
-    - **height**: The desired height of the image generated by the model. The default is 512. Any positive multiple of 8 is accepted.
+    - **height**: The desired height of the image generated by the model. The default is 512. Must be a multiple of 8.
-    - **width**: The desired width of the image generated by the model. The default is 512. Any positive multiple of 8 is accepted.
+    - **width**: The desired width of the image generated by the model. The default is 512. Must be a multiple of 8.
    - **batch_size**: The batch size of generated images. If you're planning to generate batches of images, you can try
      increasing this value to speed up the generation process. The default is 1.
    - **attention_implementation**: The attention implementation used when converting the model. Choose SPLIT_EINSUM or
@@ -307,8 +284,8 @@ can use any CLIP or VAE model as long as it's compatible with Stable Diffusion v
 1. **Loading text encoder (CLIP) and VAE models separately**
    - This workflow uses CLIP and VAE models available
-      [here](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/resolve/main/text_encoder/model.safetensors) and
+      [here](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/text_encoder/model.safetensors) and
-      [here](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/resolve/main/vae/diffusion_pytorch_model.safetensors).
+      [here](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/vae/diffusion_pytorch_model.safetensors).
      Once downloaded, place the models in the`models/clip` and `models/vae` directories respectively.
    - The Core ML UNet model is available
      [here](https://huggingface.co/coreml-community/coreml-stable-diffusion-v1-5_cn/blob/main/split_einsum/stable-diffusion-_v1-5_split-einsum_cn.zip).
@@ -316,7 +293,7 @@ can use any CLIP or VAE model as long as it's compatible with Stable Diffusion v
      ![coreml-unet+clip+vae](./assets/unet+sampler+clip+vae.png?raw=true)
 2. **Loading text encoder (CLIP) and VAE models from checkpoint file**
    - This workflow loads the CLIP and VAE models from the checkpoint file available
-      [here](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors).
+      [here](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors).
      Once downloaded, place the model in the`models/checkpoints` directory.
    - The Core ML UNet model is available
      [here](https://huggingface.co/coreml-community/coreml-stable-diffusion-v1-5_cn/blob/main/split_einsum/stable-diffusion-_v1-5_split-einsum_cn.zip).
@@ -393,59 +370,62 @@ The models used in this workflow are available at the following links:
 ![sdxl](./assets/sdxl_conversion.png?raw=true)
 ## Quantization (opt-in)
 The `Core ML Converter` and `Core ML LCM Converter` nodes accept an
 optional `quantize_nbits` dropdown that runs k-means weight palettization
 (`coremltools.optimize.coreml.palettize_weights`) on the UNet before save.
 Values: `none` (default — no quantization, identical to unquantized
 behavior and filenames), `8`, `6`, `4`. The number is appended to the
 .mlpackage stem as `_q<bits>` so quantized and unquantized variants
 coexist on disk and in cache.
 ### SD1.5 1×512×512 SPLIT_EINSUM tradeoffs (M2 Pro, ANE)
 Measured with 20 UNet forward passes at a fixed seed for the PSNR
 comparison:
 | nbits | size (MB) | size vs none | fwd median (ms) | PSNR vs `none` (dB) |
 |---|---:|---:|---:|---:|
 | none | 1641 | 1.000 | 197.1 | — |
 | 8    |  822 | 0.501 | 186.6 | 53.5 |
 | 6    |  617 | 0.376 | 183.0 | 40.2 |
 | 4    |  412 | 0.251 | 179.8 | 27.5 |
 PSNR here is computed on the raw `noise_pred` output of a single UNet
 forward at a fixed seed, not on the final decoded image — it isolates
 the quantization-induced drift from sampler / VAE noise. Final-image
 PSNR is comfortably higher (the sampler averages over 20 steps).
 ### Recommended settings per chip / RAM
 - **8 GB RAM (M1 base, M2 base):** `nbits=4`. ~4× smaller model, still
  loads, PSNR 27 dB is visually identical at SD1.5 sizes.
 - **16 GB RAM (M1/M2/M3 Pro):** `nbits=6` is the sweet spot — ~2.7×
  smaller, PSNR 40 dB, no perceptible quality drop.
 - **32 GB+ RAM (Max / Ultra):** `nbits=8` if you want the safety
  margin, `none` if you want bit-identical output for golden testing.
 The default stays `none` so existing workflows produce byte-for-byte
 identical output.
 ## Limitations
 - Core ML models are fixed in terms of their inputs and outputs.
  This means you'll need to use latent images of the same size as the input of the model (512x512 is the default for
  SD1.5).
-  However, you can re-convert the model to a different input size using the
+  However, you can convert the model to a different input size using tools available
-  conversion nodes in this suite (set the desired width and height).
+  in the [apple/ml-stable-diffusion](https://github.com/apple/ml-stable-diffusion) repository.
 - SD2.1 models are not supported.
 [^1]:
 Unless [EnumeratedShapes](https://apple.github.io/coremltools/docs-guides/source/flexible-inputs.html#select-from-predetermined-shapes)
 is used during conversion. Needs more testing.
 ## FAQ
 ### Hardware and Performance
 #### What's the difference between MPS, GPU, and ANE?
 - **MPS (Metal Performance Shaders)**: Apple's framework for GPU acceleration. It's what PyTorch uses by default on Apple Silicon.
 - **GPU**: The graphics processing unit on your Apple Silicon chip.
 - **ANE (Apple Neural Engine)**: A specialized hardware accelerator for machine learning tasks.
 #### Which compute unit should I choose?
 - **CPU_AND_ANE**: Best for models converted with `--attention-implementation SPLIT_EINSUM`. This is the default and recommended option for most users.
 - **CPU_AND_GPU**: Best for models converted with `--attention-implementation ORIGINAL`. Use this if you experience issues with ANE.
 - **CPU_ONLY**: Use this as a fallback if you experience issues with both ANE and GPU.
 #### Do I need `PYTORCH_ENABLE_MPS_FALLBACK=1`?
 While our Core ML nodes don't use this environment variable directly, it may still be relevant for other parts of ComfyUI that use PyTorch with MPS backend. The setting of this variable is a user preference and depends on your specific needs and workflow requirements.
 ### Model Conversion and Compatibility
 #### Is there a performance penalty when using the Core ML Adapter?
 Yes, there might be a slight performance penalty compared to using directly converted models. However, the adapter provides more flexibility and compatibility with standard ComfyUI nodes.
 #### Does the Core ML Adapter support SDXL?
 Currently, SDXL support in the Core ML Adapter is limited. While it may work with some models, it's not officially supported and may cause issues.
 #### Are `mlmodelc` and `mlpackage` formats safe?
 Yes, both formats are safe to use. However, we recommend:
 1. Always downloading original `.safetensors` files from trusted sources
 2. Converting them yourself using our tools
 3. Using the converted `.mlmodelc` files for better performance
 #### Do Core ML models produce identical results to their safetensors counterparts?
 While the results should be very similar, there might be slight differences due to:
 - Different numerical precision
 - Hardware-specific optimizations
 - Different attention implementations
 #### Should I convert models every time I queue a generation?
 No! The conversion only happens once when you first use the converter node. After that, you should use the `CoreMLUnetLoader` to load the already converted model.
 #### Will SDXL ever be supported on ANE?
 Currently, there are technical limitations preventing SDXL from running efficiently on ANE. We recommend using `CPU_AND_GPU` or `CPU_ONLY` for SDXL models.
 ## Support
 I'm here to help! If you have any questions or suggestions, don't hesitate to open an issue and I'll do my best
@@ -11,6 +11,9 @@ from coreml_suite.nodes import (
    CoreMLConverter,
    COREML_LOAD_LORA,
 )
 from coreml_suite.lcm import (
    COREML_CONVERT_LCM,
 )
 NODE_CLASS_MAPPINGS = {
    "CoreMLUNetLoader": CoreMLLoaderUNet,
@@ -19,6 +22,7 @@ NODE_CLASS_MAPPINGS = {
    "CoreMLModelAdapter": CoreMLModelAdapter,
    "Core ML LoRA Loader": COREML_LOAD_LORA,
    "Core ML Converter": CoreMLConverter,
    "Core ML LCM Converter": COREML_CONVERT_LCM,
 }
 NODE_DISPLAY_NAME_MAPPINGS = {
    "CoreMLUNetLoader": "Load Core ML UNet",
@@ -27,4 +31,5 @@ NODE_DISPLAY_NAME_MAPPINGS = {
    "CoreMLModelAdapter": "Core ML Adapter (Experimental)",
    "Core ML LoRA Loader": "Load LoRA to use with Core ML",
    "Core ML Converter": "Convert Checkpoint to Core ML",
    "Core ML LCM Converter": "Convert LCM to Core ML",
 }
@@ -1,4 +0,0 @@
 """Top-level conftest: prevent pytest from importing the repo-root
 __init__.py (the ComfyUI custom-node entry point pulls in comfy + nodes,
 which breaks the Tier-0 'no-framework' promise)."""
 collect_ignore = ["__init__.py"]
@@ -1,18 +0,0 @@
 # Toolchain ceiling for installing a floating-latest ComfyUI's requirements.txt
 # in the Tier 2 nightly canary (.github/workflows/tier2.yml, latest mode).
 #
 # ComfyUI's requirements.txt requests bare `torch`/`torchvision`/`torchaudio`
 # and `numpy>=1.25.0`, which would float past the versions coremltools 9 /
 # apple-ml-stable-diffusion have been validated against.
 # These constraints cap the resolution so the canary keeps testing the same
 # toolchain the suite actually ships.
 #
 # If upstream ComfyUI ever hard-requires something beyond these bounds, the
 # install FAILS — and that failure is the signal we want: it means the host
 # outgrew the pinned toolchain and coremltools / ml-stable-diffusion need a
 # deliberate bump, not a silent float.
 torch>=2.7,<2.8
 torchvision>=0.22,<0.23
 torchaudio>=2.7,<2.8
 numpy>=1.25,<2
 coremltools>=9,<10
@@ -1,10 +1,17 @@
 from enum import Enum
 import torch
 from comfy import supported_models_base
 from comfy import latent_formats
 from comfy.model_detection import convert_config
-from coreml_diffusion import ModelVersion
+
 class ModelVersion(Enum):
    SD15 = "sd15"
    SDXL = "sdxl"
    SDXL_REFINER = "sdxl_refiner"
    LCM = "lcm"
 config_map = {
@@ -1,14 +1,62 @@
-"""Compatibility shim — re-exports from coreml_suite.core.controlnet."""
+from itertools import chain
-from coreml_suite.core.controlnet import (
+from math import ceil
    chunk_control,
    expand_inputs,
    extract_residual_kwargs,
    no_control,
 )
-__all__ = [
+import numpy as np
-    "chunk_control",
+import torch
-    "expand_inputs",
+
-    "extract_residual_kwargs",
+from coreml_suite.latents import chunk_batch
-    "no_control",
+
-]
+
 def expand_inputs(inputs):
    expanded = inputs.copy()
    for k, v in inputs.items():
        if isinstance(v, np.ndarray):
            expanded[k] = np.concatenate([v] * 2) if v.shape[0] == 1 else v
        elif isinstance(v, torch.Tensor):
            expanded[k] = torch.cat([v] * 2) if v.shape[0] == 1 else v
        elif isinstance(v, list):
            expanded[k] = v * 2 if len(v) == 1 else v
        elif isinstance(v, dict):
            expand_inputs(v)
    return expanded
 def extract_residual_kwargs(expected_inputs, control):
    if "additional_residual_0" not in expected_inputs.keys():
        return {}
    if control is None:
        return no_control(expected_inputs)
    residual_kwargs = {
        "additional_residual_{}".format(i): r.cpu().numpy().astype(np.float16)
        for i, r in enumerate(chain(control["output"], control["middle"]))
    }
    return residual_kwargs
 def no_control(expected_inputs):
    shapes_dict = {
        k: v["shape"] for k, v in expected_inputs.items() if k.startswith("additional")
    }
    residual_kwargs = {
        k: torch.zeros(*shape).cpu().numpy().astype(dtype=np.float16)
        for k, shape in shapes_dict.items()
    }
    return residual_kwargs
 def chunk_control(cn, target_size):
    if cn is None:
        return [None] * target_size
    num_chunks = ceil(cn["output"][0].shape[0] / target_size)
    out = [{"output": [], "middle": []} for _ in range(num_chunks)]
    for k, v in cn.items():
        for i, x in enumerate(v):
            chunks = chunk_batch(x, (target_size, *x.shape[1:]))
            for j, chunk in enumerate(chunks):
                out[j][k].append(chunk)
    return out
@@ -0,0 +1,362 @@
 import gc
 import os
 import shutil
 import time
 from typing import Union
 import coremltools as ct
 import numpy as np
 import python_coreml_stable_diffusion.unet
 import torch
 from diffusers import (
    StableDiffusionPipeline,
    LatentConsistencyModelPipeline,
    StableDiffusionXLPipeline,
 )
 from python_coreml_stable_diffusion.unet import (
    UNet2DConditionModel,
    UNet2DConditionModelXL,
    AttentionImplementations,
 )
 from coreml_suite.config import ModelVersion
 from coreml_suite.lcm.unet import UNet2DConditionModelLCM
 from coreml_suite.logger import logger
 from folder_paths import get_folder_paths
 class StableDiffusionLCMPipeline(LatentConsistencyModelPipeline):
    pass
 MODEL_TYPE_TO_UNET_CLS = {
    ModelVersion.SD15: UNet2DConditionModel,
    ModelVersion.SDXL: UNet2DConditionModelXL,
    ModelVersion.LCM: UNet2DConditionModelLCM,
 }
 MODEL_TYPE_TO_PIPE_CLS = {
    ModelVersion.SD15: StableDiffusionPipeline,
    ModelVersion.SDXL: StableDiffusionXLPipeline,
    ModelVersion.LCM: StableDiffusionLCMPipeline,
 }
 def get_unet(model_type: ModelVersion, ref_pipe):
    ref_unet = ref_pipe.unet
    unet_cls = MODEL_TYPE_TO_UNET_CLS[model_type]
    cml_unet = unet_cls.from_config(ref_unet.config).eval()
    cml_unet.load_state_dict(ref_unet.state_dict(), strict=False)
    return cml_unet
 def get_encoder_hidden_states_shape(ref_pipe, batch_size):
    text_encoder = (
        ref_pipe.text_encoder_2
        if hasattr(ref_pipe, "text_encoder_2")
        else ref_pipe.text_encoder
    )
    text_token_sequence_length = text_encoder.config.max_position_embeddings
    hidden_size = (text_encoder.config.hidden_size,)
    encoder_hidden_states_shape = (
        batch_size,
        ref_pipe.unet.config.cross_attention_dim or hidden_size,
        1,
        text_token_sequence_length,
    )
    return encoder_hidden_states_shape
 def get_coreml_inputs(sample_inputs):
    coreml_sample_unet_inputs = {
        k: v.numpy().astype(np.float16) for k, v in sample_inputs.items()
    }
    return [
        ct.TensorType(
            name=k,
            shape=v.shape,
            dtype=v.numpy().dtype if isinstance(v, torch.Tensor) else v.dtype,
        )
        for k, v in coreml_sample_unet_inputs.items()
    ]
 def load_coreml_model(out_path):
    logger.info(f"Loading model from {out_path}")
    start = time.time()
    coreml_model = ct.models.MLModel(out_path)
    logger.info(f"Loading {out_path} took {time.time() - start:.1f} seconds")
    return coreml_model
 def convert_to_coreml(
    submodule_name, torchscript_module, sample_inputs, output_names, out_path
 ):
    if os.path.exists(out_path):
        logger.info(f"Skipping export because {out_path} already exists")
        coreml_model = load_coreml_model(out_path)
    else:
        logger.info(f"Converting {submodule_name} to CoreML..")
        coreml_model = ct.convert(
            torchscript_module,
            convert_to="mlprogram",
            minimum_deployment_target=ct.target.macOS13,
            inputs=sample_inputs,
            outputs=[
                ct.TensorType(name=name, dtype=np.float32) for name in output_names
            ],
            skip_model_load=True,
        )
        del torchscript_module
        gc.collect()
    return coreml_model
 def get_out_path(submodule_name, model_name):
    fname = f"{model_name}_{submodule_name}.mlpackage"
    unet_path = get_folder_paths(submodule_name)[0]
    out_path = os.path.join(unet_path, fname)
    return out_path
 def compile_coreml_model(source_model_path, output_dir, final_name):
    """Compiles Core ML models using the coremlcompiler utility from Xcode toolchain"""
    target_path = os.path.join(output_dir, f"{final_name}.mlmodelc")
    if os.path.exists(target_path):
        logger.warning(f"Found existing compiled model at {target_path}! Skipping..")
        return target_path
    logger.info(f"Compiling {source_model_path}")
    source_model_name = os.path.basename(os.path.splitext(source_model_path)[0])
    os.system(f"xcrun coremlcompiler compile {source_model_path} {output_dir}")
    compiled_output = os.path.join(output_dir, f"{source_model_name}.mlmodelc")
    shutil.move(compiled_output, target_path)
    return target_path
 def get_sample_input(batch_size, encoder_hidden_states_shape, sample_shape, scheduler):
    sample_unet_inputs = dict(
        [
            ("sample", torch.rand(*sample_shape)),
            (
                "timestep",
                torch.tensor([scheduler.timesteps[0].item()] * batch_size).to(
                    torch.float32
                ),
            ),
            ("encoder_hidden_states", torch.rand(*encoder_hidden_states_shape)),
        ]
    )
    return sample_unet_inputs
 def lcm_inputs(sample_unet_inputs):
    batch_size = sample_unet_inputs["sample"].shape[0]
    return {"timestep_cond": torch.randn(batch_size, 256).to(torch.float32)}
 def sdxl_inputs(sample_unet_inputs, ref_pipe):
    sample_shape = sample_unet_inputs["sample"].shape
    batch_size = sample_shape[0]
    h = sample_shape[2] * 8
    w = sample_shape[3] * 8
    original_size = (h, w)
    crops_coords_top_left = (0, 0)
    is_refiner = (
        hasattr(ref_pipe.config, "requires_aesthetics_score")
        and ref_pipe.config.requires_aesthetics_score
    )
    if is_refiner:
        aesthetic_score = (6.0,)
        time_ids_list = list(original_size + crops_coords_top_left + aesthetic_score)
    else:
        target_size = (h, w)
        time_ids_list = list(original_size + crops_coords_top_left + target_size)
    time_ids = torch.tensor(time_ids_list).repeat(batch_size, 1).to(torch.int64)
    text_embeds_shape = (batch_size, ref_pipe.text_encoder_2.config.hidden_size)
    return {
        "time_ids": time_ids,
        "text_embeds": torch.randn(*text_embeds_shape).to(torch.float32),
    }
 def get_inputs_spec(inputs):
    inputs_spec = {k: (v.shape, v.dtype) for k, v in inputs.items()}
    return inputs_spec
 def add_cnet_support(sample_shape, reference_unet):
    from python_coreml_stable_diffusion.unet import calculate_conv2d_output_shape
    additional_residuals_shapes = []
    batch_size = sample_shape[0]
    h, w = sample_shape[2:]
    # conv_in
    out_h, out_w = calculate_conv2d_output_shape(
        h,
        w,
        reference_unet.conv_in,
    )
    additional_residuals_shapes.append(
        (batch_size, reference_unet.conv_in.out_channels, out_h, out_w)
    )
    # down_blocks
    for down_block in reference_unet.down_blocks:
        additional_residuals_shapes += [
            (batch_size, resnet.out_channels, out_h, out_w)
            for resnet in down_block.resnets
        ]
        if hasattr(down_block, "downsamplers") and down_block.downsamplers is not None:
            for downsampler in down_block.downsamplers:
                out_h, out_w = calculate_conv2d_output_shape(
                    out_h, out_w, downsampler.conv
                )
            additional_residuals_shapes.append(
                (
                    batch_size,
                    down_block.downsamplers[-1].conv.out_channels,
                    out_h,
                    out_w,
                )
            )
    # mid_block
    additional_residuals_shapes.append(
        (batch_size, reference_unet.mid_block.resnets[-1].out_channels, out_h, out_w)
    )
    additional_inputs = {}
    for i, shape in enumerate(additional_residuals_shapes):
        sample_residual_input = torch.rand(*shape)
        additional_inputs[f"additional_residual_{i}"] = sample_residual_input
    return additional_inputs
 def convert_unet(
    ref_pipe,
    model_version: ModelVersion,
    unet_out_path: str,
    batch_size: int = 1,
    sample_size: tuple[int, int] = (64, 64),
    controlnet_support: bool = False,
 ):
    coreml_unet = get_unet(model_version, ref_pipe)
    ref_unet = ref_pipe.unet
    sample_shape = (
        batch_size,  # B
        ref_unet.config.in_channels,  # C
        sample_size[0],  # H
        sample_size[1],  # W
    )
    encoder_hidden_states_shape = get_encoder_hidden_states_shape(ref_pipe, batch_size)
    scheduler = ref_pipe.scheduler
    scheduler.set_timesteps(50)
    sample_inputs = get_sample_input(
        batch_size, encoder_hidden_states_shape, sample_shape, scheduler
    )
    if model_version == ModelVersion.LCM:
        sample_inputs |= lcm_inputs(sample_inputs)
    if model_version == ModelVersion.SDXL:
        sample_inputs |= sdxl_inputs(sample_inputs, ref_pipe)
    if controlnet_support:
        sample_inputs |= add_cnet_support(sample_shape, ref_unet)
    sample_inputs_spec = get_inputs_spec(sample_inputs)
    logger.info(f"Sample UNet inputs spec: {sample_inputs_spec}")
    logger.info("JIT tracing..")
    traced_unet = torch.jit.trace(
        coreml_unet, example_inputs=list(sample_inputs.values())
    )
    logger.info("Done.")
    coreml_sample_inputs = get_coreml_inputs(sample_inputs)
    coreml_unet = convert_to_coreml(
        "unet", traced_unet, coreml_sample_inputs, ["noise_pred"], unet_out_path
    )
    del traced_unet
    gc.collect()
    coreml_unet.save(unet_out_path)
    logger.info(f"Saved unet into {unet_out_path}")
 def convert(
    ckpt_path: str,
    model_version: ModelVersion,
    unet_out_path: str,
    batch_size: int = 1,
    sample_size: tuple[int, int] = (64, 64),
    controlnet_support: bool = False,
    lora_weights: list[tuple[Union[str, os.PathLike], float]] = None,
    attn_impl: str = AttentionImplementations.SPLIT_EINSUM.name,
    config_path: str = None,
 ):
    if os.path.exists(unet_out_path):
        logger.info(f"Found existing model at {unet_out_path}! Skipping..")
        return
    python_coreml_stable_diffusion.unet.ATTENTION_IMPLEMENTATION_IN_EFFECT = (
        AttentionImplementations(attn_impl)
    )
    ref_pipe = get_pipeline(ckpt_path, config_path, model_version)
    for i, lora_weight in enumerate(lora_weights or []):
        lora_path, strength = lora_weight
        adapter_name = f"lora_{i}"
        ref_pipe.load_lora_weights(lora_path, adapter_name=adapter_name)
        ref_pipe.set_adapters([adapter_name], adapter_weights=[strength])
        ref_pipe.fuse_lora()
    convert_unet(
        ref_pipe,
        model_version,
        unet_out_path,
        batch_size,
        sample_size,
        controlnet_support,
    )
 def get_pipeline(ckpt_path, config_path, model_version):
    pipe_cls = MODEL_TYPE_TO_PIPE_CLS[model_version]
    ref_pipe = pipe_cls.from_single_file(ckpt_path, original_config_file=config_path)
    return ref_pipe
 def compile_model(out_path, out_name, submodule_name):
    # Compile the model
    target_path = compile_coreml_model(
        out_path, get_folder_paths(submodule_name)[0], f"{out_name}_{submodule_name}"
    )
    logger.info(f"Compiled {out_path} to {target_path}")
    return target_path
@@ -1,10 +0,0 @@
 """Framework-free pure-logic core of ComfyUI-CoreMLSuite.
 Modules under this package must NOT import `comfy`, `coremltools`,
 `python_coreml_stable_diffusion`, `folder_paths`, `nodes`, or any other
 ComfyUI / Apple runtime. Only `numpy` and `torch` are allowed.
 The thin adapters in `coreml_suite.{latents,controlnet,models}` keep the
 old public import paths working so `coreml_suite/nodes.py` and downstream
 ComfyUI workflows are unchanged.
 """
@@ -1,67 +0,0 @@
 """Pure helpers around the ControlNet residual inputs of the Core ML UNet.
 Re-exported by coreml_suite.controlnet. Characterization tests cover
 shapes, dtype (fp16), and zero-fill fallback.
 """
 from itertools import chain
 from math import ceil
 import numpy as np
 import torch
 from coreml_suite.core.latents import chunk_batch
 def expand_inputs(inputs):
    expanded = inputs.copy()
    for k, v in inputs.items():
        if isinstance(v, np.ndarray):
            expanded[k] = np.concatenate([v] * 2) if v.shape[0] == 1 else v
        elif isinstance(v, torch.Tensor):
            expanded[k] = torch.cat([v] * 2) if v.shape[0] == 1 else v
        elif isinstance(v, list):
            expanded[k] = v * 2 if len(v) == 1 else v
        elif isinstance(v, dict):
            expand_inputs(v)
    return expanded
 def extract_residual_kwargs(expected_inputs, control):
    if "additional_residual_0" not in expected_inputs.keys():
        return {}
    if control is None:
        return no_control(expected_inputs)
    residual_kwargs = {
        "additional_residual_{}".format(i): r.cpu().numpy().astype(np.float16)
        for i, r in enumerate(chain(control["output"], control["middle"]))
    }
    return residual_kwargs
 def no_control(expected_inputs):
    shapes_dict = {
        k: v["shape"] for k, v in expected_inputs.items() if k.startswith("additional")
    }
    residual_kwargs = {
        k: torch.zeros(*shape).cpu().numpy().astype(dtype=np.float16)
        for k, shape in shapes_dict.items()
    }
    return residual_kwargs
 def chunk_control(cn, target_size):
    if cn is None:
        return [None] * target_size
    num_chunks = ceil(cn["output"][0].shape[0] / target_size)
    out = [{"output": [], "middle": []} for _ in range(num_chunks)]
    for k, v in cn.items():
        for i, x in enumerate(v):
            chunks = chunk_batch(x, (target_size, *x.shape[1:]))
            for j, chunk in enumerate(chunks):
                out[j][k].append(chunk)
    return out
@@ -1,111 +0,0 @@
 """Pure transform from torch sampler inputs to Core ML UNet kwargs.
 Characterization tests cover SD1.5 / SDXL base / SDXL refiner / LCM
 variants and the chunked-batch fan-out.
 """
 import numpy as np
 import torch
 from coreml_suite.core.controlnet import extract_residual_kwargs, chunk_control
 from coreml_suite.core.latents import chunk_batch
 class CoreMLInputs:
    def __init__(self, x, t, context, control, **kwargs):
        self.x = x
        self.t = t
        self.context = context
        self.control = control
        self.time_ids = kwargs.get("time_ids")
        self.text_embeds = kwargs.get("text_embeds")
        self.ts_cond = kwargs.get("timestep_cond")
    def coreml_kwargs(self, expected_inputs):
        sample = self.x.cpu().numpy().astype(np.float16)
        context = self.context.cpu().numpy().astype(np.float16)
        t = self.t.cpu().numpy().astype(np.float16)
        model_input_kwargs = {
            "sample": sample,
            "encoder_hidden_states": context,
            "timestep": t,
        }
        residual_kwargs = extract_residual_kwargs(expected_inputs, self.control)
        model_input_kwargs |= residual_kwargs
        # LCM
        if self.ts_cond is not None:
            model_input_kwargs["timestep_cond"] = (
                self.ts_cond.cpu().numpy().astype(np.float16)
            )
        # SDXL
        if "text_embeds" in expected_inputs:
            model_input_kwargs["text_embeds"] = (
                self.text_embeds.cpu().numpy().astype(np.float16)
            )
        if "time_ids" in expected_inputs:
            model_input_kwargs["time_ids"] = (
                self.time_ids.cpu().numpy().astype(np.float16)
            )
        return model_input_kwargs
    def chunks(self, expected_inputs):
        sample_shape = expected_inputs["sample"]["shape"]
        timestep_shape = expected_inputs["timestep"]["shape"]
        context_shape = expected_inputs["encoder_hidden_states"]["shape"]
        chunked_x = chunk_batch(self.x, sample_shape)
        ts = list(torch.full((len(chunked_x), timestep_shape[0]), self.t[0]))
        chunked_context = chunk_batch(self.context, context_shape)
        chunked_control = [None] * len(chunked_x)
        if self.control is not None:
            chunked_control = chunk_control(self.control, sample_shape[0])
        chunked_ts_cond = [None] * len(chunked_x)
        if self.ts_cond is not None:
            ts_cond_shape = expected_inputs["timestep_cond"]["shape"]
            chunked_ts_cond = chunk_batch(self.ts_cond, ts_cond_shape)
        chunked_time_ids = [None] * len(chunked_x)
        if expected_inputs.get("time_ids") is not None:
            time_ids_shape = expected_inputs["time_ids"]["shape"]
            if self.time_ids is None:
                self.time_ids = torch.zeros(len(chunked_x), *time_ids_shape[1:]).to(
                    self.x.device
                )
            chunked_time_ids = chunk_batch(self.time_ids, time_ids_shape)
        chunked_text_embeds = [None] * len(chunked_x)
        if expected_inputs.get("text_embeds") is not None:
            text_embeds_shape = expected_inputs["text_embeds"]["shape"]
            if self.text_embeds is None:
                self.text_embeds = torch.zeros(
                    len(chunked_x), *text_embeds_shape[1:]
                ).to(self.x.device)
            chunked_text_embeds = chunk_batch(self.text_embeds, text_embeds_shape)
        return [
            CoreMLInputs(
                x,
                t,
                context,
                control,
                timestep_cond=ts_cond,
                time_ids=time_ids,
                text_embeds=text_embeds,
            )
            for x, t, context, control, ts_cond, time_ids, text_embeds in zip(
                chunked_x,
                ts,
                chunked_context,
                chunked_control,
                chunked_ts_cond,
                chunked_time_ids,
                chunked_text_embeds,
            )
        ]
@@ -1,42 +0,0 @@
 """Pure batch-chunking helpers for Core ML's fixed-shape UNet inputs.
 Re-exported by coreml_suite.latents. Characterization tests cover the
 contract (padding-zero regions, truncation in merge_chunks,
 identity-passthrough when shape already matches).
 """
 import torch
 def chunk_batch(input_tensor, target_shape):
    if input_tensor.shape == target_shape:
        return [input_tensor]
    batch_size = input_tensor.shape[0]
    target_batch_size = target_shape[0]
    num_chunks = batch_size // target_batch_size
    if num_chunks == 0:
        padding = torch.zeros(target_batch_size - batch_size, *target_shape[1:]).to(
            input_tensor.device
        )
        return [torch.cat((input_tensor, padding), dim=0)]
    mod = batch_size % target_batch_size
    if mod != 0:
        chunks = list(torch.chunk(input_tensor[:-mod], num_chunks))
        padding = torch.zeros(target_batch_size - mod, *target_shape[1:]).to(
            input_tensor.device
        )
        padded = torch.cat((input_tensor[-mod:], padding), dim=0)
        chunks.append(padded)
        return chunks
    chunks = list(torch.chunk(input_tensor, num_chunks))
    return chunks
 def merge_chunks(chunks, orig_shape):
    merged = torch.cat(chunks, dim=0)
    if merged.shape == orig_shape:
        return merged
    return merged[: orig_shape[0]]
@@ -1,91 +0,0 @@
 """Pure SDXL detection + time_ids/text_embeds assembly.
 The framework-coupled adapter `add_sdxl_model_options` lives in models.py
 and delegates the math here. Characterization tests cover base (len 6) vs
 refiner (len 5) and the closure free-vars produced by
 `sdxl_model_function_wrapper`.
 """
 import torch
 def is_sdxl(coreml_model):
    return (
        "time_ids" in coreml_model.expected_inputs
        and "text_embeds" in coreml_model.expected_inputs
    )
 def is_sdxl_base(coreml_model):
    return (
        is_sdxl(coreml_model)
        and coreml_model.expected_inputs["time_ids"]["shape"][1] == 6
    )
 def is_sdxl_refiner(coreml_model):
    return (
        is_sdxl(coreml_model)
        and coreml_model.expected_inputs["time_ids"]["shape"][1] == 5
    )
 def build_sdxl_time_ids(pos_dict, neg_dict, *, is_base: bool, is_refiner: bool):
    """Compose the (2, N) time_ids tensor for the SDXL Core ML UNet.
    - base: N=6  -> [h, w, crop_h, crop_w, target_h, target_w]
    - refiner: N=5 -> [h, w, crop_h, crop_w, aesthetic_score]
    - neither: N=4 -> [h, w, crop_h, crop_w] (edge case kept for parity)
    """
    pos_time_ids = [
        pos_dict.get("height", 768),
        pos_dict.get("width", 768),
        pos_dict.get("crop_h", 0),
        pos_dict.get("crop_w", 0),
    ]
    neg_time_ids = [
        neg_dict.get("height", 768),
        neg_dict.get("width", 768),
        neg_dict.get("crop_h", 0),
        neg_dict.get("crop_w", 0),
    ]
    if is_base:
        pos_time_ids += [
            pos_dict.get("target_height", 768),
            pos_dict.get("target_width", 768),
        ]
        neg_time_ids += [
            neg_dict.get("target_height", 768),
            neg_dict.get("target_width", 768),
        ]
    if is_refiner:
        pos_time_ids += [pos_dict.get("aesthetic_score", 6)]
        neg_time_ids += [neg_dict.get("aesthetic_score", 2.5)]
    return torch.tensor([pos_time_ids, neg_time_ids])
 def build_sdxl_text_embeds(pos_pooled, neg_pooled):
    """Concat pos then neg along the batch dim. Locked contract."""
    return torch.cat((pos_pooled, neg_pooled))
 def sdxl_model_function_wrapper(time_ids, text_embeds, refiner=False):
    def wrapper(model_function, params):
        x = params["input"]
        t = params["timestep"]
        c = params["c"]
        context = c.get("c_crossattn")
        if context is None:
            return torch.zeros_like(x)
        if refiner and context is not None:
            # converted refiner accepts only g clip
            c["c_crossattn"] = context[:, :, 768:]
        return model_function(x, t, **c, time_ids=time_ids, text_embeds=text_embeds)
    return wrapper
@@ -1,42 +0,0 @@
 import time
 import coremltools as ct
 from coreml_suite.logger import logger
 class CoreMLModel:
    """Small runtime wrapper around coremltools.models.MLModel.
    This keeps the inference path independent from apple/ml-stable-diffusion's
    CoreMLModel wrapper while preserving the contract used by the sampler code:
    ``expected_inputs`` and callable prediction.
    """
    def __init__(self, model_path, compute_unit):
        self.model_path = model_path
        self.compute_unit = self._compute_unit(compute_unit)
        logger.info(f"Loading {model_path} to {self.compute_unit.name}")
        start = time.time()
        self.model = ct.models.MLModel(model_path, compute_units=self.compute_unit)
        logger.info(f"Loading {model_path} took {time.time() - start:.1f} seconds")
        self.expected_inputs = self._expected_inputs()
    def __call__(self, **kwargs):
        return self.model.predict(kwargs)
    @staticmethod
    def _compute_unit(compute_unit):
        if isinstance(compute_unit, ct.ComputeUnit):
            return compute_unit
        return ct.ComputeUnit[compute_unit]
    def _expected_inputs(self):
        return {
            feature.name: {
                "shape": tuple(feature.type.multiArrayType.shape),
            }
            for feature in self.model.get_spec().description.input
        }
@@ -1,4 +1,36 @@
-"""Compatibility shim — re-exports from coreml_suite.core.latents."""
+import torch
 from coreml_suite.core.latents import chunk_batch, merge_chunks
-__all__ = ["chunk_batch", "merge_chunks"]
+
 def chunk_batch(input_tensor, target_shape):
    if input_tensor.shape == target_shape:
        return [input_tensor]
    batch_size = input_tensor.shape[0]
    target_batch_size = target_shape[0]
    num_chunks = batch_size // target_batch_size
    if num_chunks == 0:
        padding = torch.zeros(target_batch_size - batch_size, *target_shape[1:]).to(
            input_tensor.device
        )
        return [torch.cat((input_tensor, padding), dim=0)]
    mod = batch_size % target_batch_size
    if mod != 0:
        chunks = list(torch.chunk(input_tensor[:-mod], num_chunks))
        padding = torch.zeros(target_batch_size - mod, *target_shape[1:]).to(
            input_tensor.device
        )
        padded = torch.cat((input_tensor[-mod:], padding), dim=0)
        chunks.append(padded)
        return chunks
    chunks = list(torch.chunk(input_tensor, num_chunks))
    return chunks
 def merge_chunks(chunks, orig_shape):
    merged = torch.cat(chunks, dim=0)
    if merged.shape == orig_shape:
        return merged
    return merged[: orig_shape[0]]
@@ -1,8 +1,3 @@
-"""LCM runtime support (sampler-side).
+from .nodes import COREML_CONVERT_LCM
-The dedicated LCM converter node was removed once the standard ``CoreMLConverter``
+__all__ = ["COREML_CONVERT_LCM"]
 gained model-version auto-detection (full-distill LCM is detected from the
 checkpoint). What remains here is runtime sampling support — ``utils`` patches the
 model sampling and supplies the guidance embedding when a converted UNet exposes
 ``timestep_cond``.
 """
@@ -0,0 +1,297 @@
 import os
 import shutil
 import logging
 import time
 import gc
 import numpy as np
 import torch
 from diffusers import UNet2DConditionModel, LCMScheduler
 from diffusers.loaders import LoraLoaderMixin
 from comfy.model_management import get_torch_device
 from coreml_suite.lcm.unet import UNet2DConditionModelLCM
 from transformers import CLIPTextModel
 import coremltools as ct
 from folder_paths import get_folder_paths
 logging.basicConfig()
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.DEBUG)
 MODEL_VERSION = "SimianLuo/LCM_Dreamshaper_v7"
 MODEL_NAME = MODEL_VERSION.split("/")[-1] + "_4k"
 import python_coreml_stable_diffusion.unet as unet
 unet.ATTENTION_IMPLEMENTATION_IN_EFFECT = unet.AttentionImplementations.SPLIT_EINSUM
 def get_unets():
    ref_unet = UNet2DConditionModel.from_pretrained(
        MODEL_VERSION,
        subfolder="unet",
        device_map=None,
        low_cpu_mem_usage=False,
    )
    cml_unet = UNet2DConditionModelLCM.from_config(ref_unet.config).eval()
    cml_unet.load_state_dict(ref_unet.state_dict(), strict=False)
    return cml_unet, ref_unet
 def get_encoder_hidden_states_shape(unet_config, batch_size):
    text_encoder = CLIPTextModel.from_pretrained(
        MODEL_VERSION, subfolder="text_encoder"
    )
    text_token_sequence_length = text_encoder.config.max_position_embeddings
    hidden_size = (text_encoder.config.hidden_size,)
    encoder_hidden_states_shape = (
        batch_size,
        unet_config.cross_attention_dim or hidden_size,
        1,
        text_token_sequence_length,
    )
    return encoder_hidden_states_shape
 def get_scheduler():
    scheduler = LCMScheduler.from_pretrained(MODEL_VERSION, subfolder="scheduler")
    scheduler.set_timesteps(50, get_torch_device(), 50)
    return scheduler
 def get_coreml_inputs(sample_inputs):
    coreml_sample_unet_inputs = {
        k: v.numpy().astype(np.float16) for k, v in sample_inputs.items()
    }
    return [
        ct.TensorType(
            name=k,
            shape=v.shape,
            dtype=v.numpy().dtype if isinstance(v, torch.Tensor) else v.dtype,
        )
        for k, v in coreml_sample_unet_inputs.items()
    ]
 def load_coreml_model(out_path):
    logger.info(f"Loading model from {out_path}")
    start = time.time()
    coreml_model = ct.models.MLModel(out_path)
    logger.info(f"Loading {out_path} took {time.time() - start:.1f} seconds")
    return coreml_model
 def convert_to_coreml(
    submodule_name, torchscript_module, sample_inputs, output_names, out_path
 ):
    if os.path.exists(out_path):
        logger.info(f"Skipping export because {out_path} already exists")
        coreml_model = load_coreml_model(out_path)
    else:
        logger.info(f"Converting {submodule_name} to CoreML..")
        coreml_model = ct.convert(
            torchscript_module,
            convert_to="mlprogram",
            minimum_deployment_target=ct.target.macOS13,
            inputs=sample_inputs,
            outputs=[
                ct.TensorType(name=name, dtype=np.float32) for name in output_names
            ],
            skip_model_load=True,
        )
        del torchscript_module
        gc.collect()
    return coreml_model
 def get_out_path(submodule_name, model_name):
    fname = f"{model_name}_{submodule_name}.mlpackage"
    unet_path = get_folder_paths(submodule_name)[0]
    out_path = os.path.join(unet_path, fname)
    return out_path
 def compile_coreml_model(source_model_path, output_dir, final_name):
    """Compiles Core ML models using the coremlcompiler utility from Xcode toolchain"""
    target_path = os.path.join(output_dir, f"{final_name}.mlmodelc")
    if os.path.exists(target_path):
        logger.warning(f"Found existing compiled model at {target_path}! Skipping..")
        return target_path
    logger.info(f"Compiling {source_model_path}")
    source_model_name = os.path.basename(os.path.splitext(source_model_path)[0])
    os.system(f"xcrun coremlcompiler compile {source_model_path} {output_dir}")
    compiled_output = os.path.join(output_dir, f"{source_model_name}.mlmodelc")
    shutil.move(compiled_output, target_path)
    return target_path
 def get_sample_input(batch_size, encoder_hidden_states_shape, sample_shape, scheduler):
    sample_unet_inputs = dict(
        [
            ("sample", torch.rand(*sample_shape)),
            (
                "timestep",
                torch.tensor([scheduler.timesteps[0].item()] * batch_size).to(
                    torch.float32
                ),
            ),
            ("encoder_hidden_states", torch.rand(*encoder_hidden_states_shape)),
            ("timestep_cond", torch.randn(batch_size, 256).to(torch.float32)),
        ]
    )
    return sample_unet_inputs
 def get_unet_inputs_spec(sample_unet_inputs):
    sample_unet_inputs_spec = {
        k: (v.shape, v.dtype) for k, v in sample_unet_inputs.items()
    }
    return sample_unet_inputs_spec
 def add_cnet_support(sample_shape, reference_unet):
    from python_coreml_stable_diffusion.unet import calculate_conv2d_output_shape
    additional_residuals_shapes = []
    batch_size = sample_shape[0]
    h, w = sample_shape[2:]
    # conv_in
    out_h, out_w = calculate_conv2d_output_shape(
        h,
        w,
        reference_unet.conv_in,
    )
    additional_residuals_shapes.append(
        (batch_size, reference_unet.conv_in.out_channels, out_h, out_w)
    )
    # down_blocks
    for down_block in reference_unet.down_blocks:
        additional_residuals_shapes += [
            (batch_size, resnet.out_channels, out_h, out_w)
            for resnet in down_block.resnets
        ]
        if hasattr(down_block, "downsamplers") and down_block.downsamplers is not None:
            for downsampler in down_block.downsamplers:
                out_h, out_w = calculate_conv2d_output_shape(
                    out_h, out_w, downsampler.conv
                )
            additional_residuals_shapes.append(
                (
                    batch_size,
                    down_block.downsamplers[-1].conv.out_channels,
                    out_h,
                    out_w,
                )
            )
    # mid_block
    additional_residuals_shapes.append(
        (batch_size, reference_unet.mid_block.resnets[-1].out_channels, out_h, out_w)
    )
    additional_inputs = {}
    for i, shape in enumerate(additional_residuals_shapes):
        sample_residual_input = torch.rand(*shape)
        additional_inputs[f"additional_residual_{i}"] = sample_residual_input
    return additional_inputs
 def convert(
    out_path: str,
    batch_size: int = 1,
    sample_size: tuple[int, int] = (64, 64),
    controlnet_support: bool = False,
    lora_paths: list[str] = None,
 ):
    lora_paths = lora_paths or []
    coreml_unet, ref_unet = get_unets()
    for lora_path in lora_paths:
        lora_sd, network_alphas = LoraLoaderMixin.lora_state_dict(lora_path)
        LoraLoaderMixin.load_lora_into_unet(lora_sd, network_alphas, ref_unet)
        ref_unet.fuse_lora()
    sample_shape = (
        batch_size,  # B
        ref_unet.config.in_channels,  # C
        sample_size[0],  # H
        sample_size[1],  # W
    )
    encoder_hidden_states_shape = get_encoder_hidden_states_shape(
        ref_unet.config, batch_size
    )
    scheduler = get_scheduler()
    sample_inputs = get_sample_input(
        batch_size, encoder_hidden_states_shape, sample_shape, scheduler
    )
    if controlnet_support:
        sample_inputs |= add_cnet_support(sample_shape, ref_unet)
    sample_inputs_spec = get_unet_inputs_spec(sample_inputs)
    logger.info(f"Sample UNet inputs spec: {sample_inputs_spec}")
    logger.info("JIT tracing..")
    traced_unet = torch.jit.trace(
        coreml_unet, example_inputs=list(sample_inputs.values())
    )
    logger.info("Done.")
    coreml_sample_inputs = get_coreml_inputs(sample_inputs)
    coreml_unet = convert_to_coreml(
        "unet", traced_unet, coreml_sample_inputs, ["noise_pred"], out_path
    )
    del traced_unet
    gc.collect()
    coreml_unet.save(out_path)
    logger.info(f"Saved unet into {out_path}")
 def compile_model(out_path, out_name):
    # Compile the model
    target_path = compile_coreml_model(
        out_path, get_folder_paths("unet")[0], f"{out_name}_unet"
    )
    logger.info(f"Compiled {out_path} to {target_path}")
    return target_path
 if __name__ == "__main__":
    h = 512
    w = 512
    sample_size = (h // 8, w // 8)
    batch_size = 4
    cn_support_str = "_cn" if True else ""
    out_name = f"{MODEL_NAME}_{batch_size}x{w}x{h}{cn_support_str}"
    out_path = get_out_path("unet", f"{out_name}")
    if not os.path.exists(out_path):
        convert(out_path=out_path, sample_size=sample_size, batch_size=batch_size)
    compile_model(out_path=out_path, out_name=out_name)
@@ -0,0 +1,70 @@
 import os
 from coremltools import ComputeUnit
 from python_coreml_stable_diffusion.coreml_model import CoreMLModel
 from coreml_suite import COREML_NODE
 from coreml_suite.lcm import converter as lcm_converter
 class COREML_CONVERT_LCM(COREML_NODE):
    """Converts a LCM model to Core ML."""
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "height": ("INT", {"default": 512, "min": 512, "max": 768, "step": 8}),
                "width": ("INT", {"default": 512, "min": 512, "max": 768, "step": 8}),
                "batch_size": ("INT", {"default": 1, "min": 1, "max": 64}),
                "compute_unit": (
                    [
                        ComputeUnit.CPU_AND_NE.name,
                        ComputeUnit.CPU_AND_GPU.name,
                        ComputeUnit.ALL.name,
                        ComputeUnit.CPU_ONLY.name,
                    ],
                ),
                "controlnet_support": ("BOOLEAN", {"default": False}),
            }
        }
    RETURN_TYPES = ("COREML_UNET",)
    RETURN_NAMES = ("coreml_model",)
    FUNCTION = "convert"
    def convert(self, height, width, batch_size, compute_unit, controlnet_support):
        """Converts a LCM model to Core ML.
        Args:
            height (int): Height of the target image.
            width (int): Width of the target image.
            batch_size (int): Batch size.
            compute_unit (str): Compute unit to use when loading the model.
        Returns:
            coreml_model: The converted Core ML model.
        The converted model is also saved to "models/unet" directory and
        can be loaded with the "LCMCoreMLLoaderUNet" node.
        """
        h = height
        w = width
        sample_size = (h // 8, w // 8)
        batch_size = batch_size
        cn_support_str = "_cn" if controlnet_support else ""
        out_name = f"{lcm_converter.MODEL_NAME}_{batch_size}x{w}x{h}{cn_support_str}"
        out_path = lcm_converter.get_out_path("unet", f"{out_name}")
        if not os.path.exists(out_path):
            lcm_converter.convert(
                out_path=out_path,
                sample_size=sample_size,
                batch_size=batch_size,
                controlnet_support=controlnet_support,
            )
        target_path = lcm_converter.compile_model(out_path=out_path, out_name=out_name)
        return (CoreMLModel(target_path, compute_unit, "compiled"),)
@@ -0,0 +1,99 @@
 from overrides import overrides
 from python_coreml_stable_diffusion.unet import UNet2DConditionModel, TimestepEmbedding
 class UNet2DConditionModelLCM(UNet2DConditionModel):
    def __init__(
        self,
        time_cond_proj_dim=None,
        **kwargs,
    ):
        super().__init__(**kwargs)
        timestep_input_dim = self.config.block_out_channels[0]
        time_embed_dim = self.config.block_out_channels[0] * 4
        time_embedding = TimestepEmbedding(
            timestep_input_dim, time_embed_dim, cond_proj_dim=time_cond_proj_dim
        )
        self.time_embedding = time_embedding
    @overrides(check_signature=False)
    def forward(
        self,
        sample,
        timestep,
        encoder_hidden_states,
        timestep_cond,
        *additional_residuals,
    ):
        # 0. Project (or look-up) time embeddings
        t_emb = self.time_proj(timestep)
        emb = self.time_embedding(t_emb, timestep_cond)
        # 1. center input if necessary
        if self.config.center_input_sample:
            sample = 2 * sample - 1.0
        # 2. pre-process
        sample = self.conv_in(sample)
        # 3. down
        down_block_res_samples = (sample,)
        for downsample_block in self.down_blocks:
            if (
                hasattr(downsample_block, "attentions")
                and downsample_block.attentions is not None
            ):
                sample, res_samples = downsample_block(
                    hidden_states=sample,
                    temb=emb,
                    encoder_hidden_states=encoder_hidden_states,
                )
            else:
                sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
            down_block_res_samples += res_samples
        if additional_residuals:
            new_down_block_res_samples = ()
            for i, down_block_res_sample in enumerate(down_block_res_samples):
                down_block_res_sample = down_block_res_sample + additional_residuals[i]
                new_down_block_res_samples += (down_block_res_sample,)
            down_block_res_samples = new_down_block_res_samples
        # 4. mid
        sample = self.mid_block(
            sample, emb, encoder_hidden_states=encoder_hidden_states
        )
        if additional_residuals:
            sample = sample + additional_residuals[-1]
        # 5. up
        for upsample_block in self.up_blocks:
            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
            down_block_res_samples = down_block_res_samples[
                : -len(upsample_block.resnets)
            ]
            if (
                hasattr(upsample_block, "attentions")
                and upsample_block.attentions is not None
            ):
                sample = upsample_block(
                    hidden_states=sample,
                    temb=emb,
                    res_hidden_states_tuple=res_samples,
                    encoder_hidden_states=encoder_hidden_states,
                )
            else:
                sample = upsample_block(
                    hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples
                )
        # 6. post-process
        sample = self.conv_norm_out(sample)
        sample = self.conv_act(sample)
        sample = self.conv_out(sample)
        return (sample,)
@@ -1,44 +1,15 @@
-"""Framework-coupled glue between Core ML UNets and ComfyUI's sampler stack.
+import numpy as np
 Pure math (CoreMLInputs, SDXL detection, time_ids/text_embeds assembly,
 sdxl_model_function_wrapper) lives in coreml_suite.core.*.
 This module is what touches comfy.*: model_base, ModelPatcher, the
 diffusion_model wrapper, and the maintainer-facing add_sdxl_model_options
 adapter.
 """
 import torch
 from comfy import model_base
 from comfy.model_management import get_torch_device
 from comfy.model_patcher import ModelPatcher
 from coreml_suite.config import get_model_config, ModelVersion
-from coreml_suite.core.inputs import CoreMLInputs
+from coreml_suite.controlnet import extract_residual_kwargs, chunk_control
-from coreml_suite.core.latents import merge_chunks
+from coreml_suite.latents import chunk_batch, merge_chunks
 from coreml_suite.core.sdxl import (
    build_sdxl_text_embeds,
    build_sdxl_time_ids,
    is_sdxl,
    is_sdxl_base,
    is_sdxl_refiner,
    sdxl_model_function_wrapper,
 )
 from coreml_suite.lcm.utils import is_lcm
 from coreml_suite.logger import logger
 __all__ = [
    "CoreMLInputs",
    "CoreMLModelWrapper",
    "CoreMLModelWrapperLCM",
    "add_sdxl_model_options",
    "get_latent_image",
    "get_model_patcher",
    "is_sdxl",
    "is_sdxl_base",
    "is_sdxl_refiner",
    "sdxl_model_function_wrapper",
 ]
 class CoreMLModelWrapper:
    def __init__(self, coreml_model):
@@ -97,27 +68,204 @@ class CoreMLModelWrapperLCM(CoreMLModelWrapper):
        self.config = None
 class CoreMLInputs:
    def __init__(self, x, t, context, control, **kwargs):
        self.x = x
        self.t = t
        self.context = context
        self.control = control
        self.time_ids = kwargs.get("time_ids")
        self.text_embeds = kwargs.get("text_embeds")
        self.ts_cond = kwargs.get("timestep_cond")
    def coreml_kwargs(self, expected_inputs):
        sample = self.x.cpu().numpy().astype(np.float16)
        context = self.context.cpu().numpy().astype(np.float16)
        context = context.transpose(0, 2, 1)[:, :, None, :]
        t = self.t.cpu().numpy().astype(np.float16)
        model_input_kwargs = {
            "sample": sample,
            "encoder_hidden_states": context,
            "timestep": t,
        }
        residual_kwargs = extract_residual_kwargs(expected_inputs, self.control)
        model_input_kwargs |= residual_kwargs
        # LCM
        if self.ts_cond is not None:
            model_input_kwargs["timestep_cond"] = (
                self.ts_cond.cpu().numpy().astype(np.float16)
            )
        # SDXL
        if "text_embeds" in expected_inputs:
            model_input_kwargs["text_embeds"] = (
                self.text_embeds.cpu().numpy().astype(np.float16)
            )
        if "time_ids" in expected_inputs:
            model_input_kwargs["time_ids"] = (
                self.time_ids.cpu().numpy().astype(np.float16)
            )
        return model_input_kwargs
    def chunks(self, expected_inputs):
        sample_shape = expected_inputs["sample"]["shape"]
        timestep_shape = expected_inputs["timestep"]["shape"]
        hidden_shape = expected_inputs["encoder_hidden_states"]["shape"]
        context_shape = (hidden_shape[0], hidden_shape[3], hidden_shape[1])
        chunked_x = chunk_batch(self.x, sample_shape)
        ts = list(torch.full((len(chunked_x), timestep_shape[0]), self.t[0]))
        chunked_context = chunk_batch(self.context, context_shape)
        chunked_control = [None] * len(chunked_x)
        if self.control is not None:
            chunked_control = chunk_control(self.control, sample_shape[0])
        chunked_ts_cond = [None] * len(chunked_x)
        if self.ts_cond is not None:
            ts_cond_shape = expected_inputs["timestep_cond"]["shape"]
            chunked_ts_cond = chunk_batch(self.ts_cond, ts_cond_shape)
        chunked_time_ids = [None] * len(chunked_x)
        if expected_inputs.get("time_ids") is not None:
            time_ids_shape = expected_inputs["time_ids"]["shape"]
            if self.time_ids is None:
                self.time_ids = torch.zeros(len(chunked_x), *time_ids_shape[1:]).to(
                    self.x.device
                )
            chunked_time_ids = chunk_batch(self.time_ids, time_ids_shape)
        chunked_text_embeds = [None] * len(chunked_x)
        if expected_inputs.get("text_embeds") is not None:
            text_embeds_shape = expected_inputs["text_embeds"]["shape"]
            if self.text_embeds is None:
                self.text_embeds = torch.zeros(
                    len(chunked_x), *text_embeds_shape[1:]
                ).to(self.x.device)
            chunked_text_embeds = chunk_batch(self.text_embeds, text_embeds_shape)
        return [
            CoreMLInputs(
                x,
                t,
                context,
                control,
                timestep_cond=ts_cond,
                time_ids=time_ids,
                text_embeds=text_embeds,
            )
            for x, t, context, control, ts_cond, time_ids, text_embeds in zip(
                chunked_x,
                ts,
                chunked_context,
                chunked_control,
                chunked_ts_cond,
                chunked_time_ids,
                chunked_text_embeds,
            )
        ]
 def is_sdxl(coreml_model):
    return (
        "time_ids" in coreml_model.expected_inputs
        and "text_embeds" in coreml_model.expected_inputs
    )
 def is_sdxl_base(coreml_model):
    return (
        is_sdxl(coreml_model)
        and coreml_model.expected_inputs["time_ids"]["shape"][1] == 6
    )
 def is_sdxl_refiner(coreml_model):
    return (
        is_sdxl(coreml_model)
        and coreml_model.expected_inputs["time_ids"]["shape"][1] == 5
    )
 def sdxl_model_function_wrapper(time_ids, text_embeds, refiner=False):
    def wrapper(model_function, params):
        x = params["input"]
        t = params["timestep"]
        c = params["c"]
        context = c.get("c_crossattn")
        if context is None:
            return torch.zeros_like(x)
        if refiner and context is not None:
            # converted refiner accepts only g clip
            c["c_crossattn"] = context[:, :, 768:]
        return model_function(x, t, **c, time_ids=time_ids, text_embeds=text_embeds)
    return wrapper
 def add_sdxl_model_options(model_patcher, positive, negative):
    mp = model_patcher.clone()
    pos_dict = positive[0][1]
    neg_dict = negative[0][1]
-    is_base = model_patcher.model.diffusion_model.is_sdxl_base
+    pos_pooled = pos_dict["pooled_output"]
    neg_pooled = neg_dict["pooled_output"]
    pos_time_ids = [
        pos_dict.get("height", 768),
        pos_dict.get("width", 768),
        pos_dict.get("crop_h", 0),
        pos_dict.get("crop_w", 0),
    ]
    neg_time_ids = [
        neg_dict.get("height", 768),
        neg_dict.get("width", 768),
        neg_dict.get("crop_h", 0),
        neg_dict.get("crop_w", 0),
    ]
    if model_patcher.model.diffusion_model.is_sdxl_base:
        pos_time_ids += [
            pos_dict.get("target_height", 768),
            pos_dict.get("target_width", 768),
        ]
        neg_time_ids += [
            neg_dict.get("target_height", 768),
            neg_dict.get("target_width", 768),
        ]
    is_refiner = model_patcher.model.diffusion_model.is_sdxl_refiner
    if is_refiner:
        pos_time_ids += [
            pos_dict.get("aesthetic_score", 6),
        ]
-    time_ids = build_sdxl_time_ids(
+        neg_time_ids += [
-        pos_dict, neg_dict, is_base=is_base, is_refiner=is_refiner
+            neg_dict.get("aesthetic_score", 2.5),
-    )
+        ]
    text_embeds = build_sdxl_text_embeds(
        pos_dict["pooled_output"], neg_dict["pooled_output"]
    )
-    mp.model_options |= {
+    time_ids = torch.tensor([pos_time_ids, neg_time_ids])
    text_embeds = torch.cat((pos_pooled, neg_pooled))
    model_options = {
        "model_function_wrapper": sdxl_model_function_wrapper(
            time_ids, text_embeds, is_refiner
        ),
    }
    mp.model_options |= model_options
    return mp
@@ -1,10 +1,13 @@
 import os
 from coremltools import ComputeUnit
 from python_coreml_stable_diffusion.coreml_model import CoreMLModel
 from python_coreml_stable_diffusion.unet import AttentionImplementations
 import folder_paths
 from coreml_suite import COREML_NODE
-from coreml_suite.coreml_model import CoreMLModel
+from coreml_suite import converter
 from coreml_suite.config import ModelVersion
 from coreml_suite.lcm.utils import add_lcm_model_options, lcm_patch, is_lcm
 from coreml_suite.logger import logger
 from nodes import KSampler, LoraLoader, KSamplerAdvanced
@@ -17,26 +20,6 @@ from coreml_suite.models import (
 )
 def _discover(fn_name, fallback):
    """Populate a converter dropdown from coreml_diffusion's discovery API.
    Fails soft: if the package is missing, too old to expose ``fn_name``, or
    errors, the node still registers with the fallback list instead of vanishing
    from the menu. Evaluated on every INPUT_TYPES call, so installing a newer
    coreml_diffusion surfaces new conversion types with no Suite change.
    """
    try:
        import coreml_diffusion
        return getattr(coreml_diffusion, fn_name)()
    except Exception as exc:  # missing/old package, import error, etc.
        logger.warning(
            f"coreml_diffusion.{fn_name} unavailable ({exc}); "
            f"using fallback {fallback}"
        )
        return fallback
 class CoreMLSampler(COREML_NODE, KSampler):
    @classmethod
    def INPUT_TYPES(s):
@@ -180,7 +163,7 @@ class CoreMLLoader(COREML_NODE):
    @classmethod
    def coreml_filenames(cls):
-        extensions = (".mlpackage",)
+        extensions = (".mlmodelc", ".mlpackage")
        all_paths = folder_paths.get_filename_list_(cls.PACKAGE_DIRNAME)[1]
        coreml_paths = folder_paths.filter_files_extensions(all_paths, extensions)
@@ -191,7 +174,9 @@ class CoreMLLoader(COREML_NODE):
        coreml_path = self.coreml_filenames()[coreml_name]
-        return (CoreMLModel(coreml_path, compute_unit),)
+        sources = "compiled" if coreml_name.endswith(".mlmodelc") else "packages"
        return (CoreMLModel(coreml_path, compute_unit, sources),)
 class CoreMLLoaderUNet(CoreMLLoader):
@@ -225,26 +210,28 @@ class CoreMLModelAdapter(COREML_NODE):
 class CoreMLConverter(COREML_NODE):
-    """Converts a Stable Diffusion checkpoint (UNet) to Core ML.
+    """Converts a LCM model to Core ML."""
    The model version (SD15 / SDXL / SDXL refiner / LCM) is auto-detected from
    the checkpoint's architecture, so there is no version dropdown — one node
    converts every supported family, including full-distill LCM.
    """
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "ckpt_name": (folder_paths.get_filename_list("checkpoints"),),
-                "height": ("INT", {"default": 512, "min": 8, "step": 8}),
+                "model_version": (
-                "width": ("INT", {"default": 512, "min": 8, "step": 8}),
+                    [
                        ModelVersion.SD15.name,
                        ModelVersion.SDXL.name,
                    ],
                ),
                "height": ("INT", {"default": 512, "min": 256, "max": 2048, "step": 8}),
                "width": ("INT", {"default": 512, "min": 256, "max": 2048, "step": 8}),
                "batch_size": ("INT", {"default": 1, "min": 1, "max": 64}),
                "attention_implementation": (
-                    _discover(
+                    [
-                        "list_attention_impls",
+                        AttentionImplementations.SPLIT_EINSUM.name,
-                        ["SPLIT_EINSUM", "SPLIT_EINSUM_V2", "ORIGINAL"],
+                        AttentionImplementations.SPLIT_EINSUM_V2.name,
-                    ),
+                        AttentionImplementations.ORIGINAL.name,
                    ],
                ),
                "compute_unit": (
                    [
@@ -257,15 +244,6 @@ class CoreMLConverter(COREML_NODE):
                "controlnet_support": ("BOOLEAN", {"default": False}),
            },
            "optional": {
                # k-means weight palettization. Kept optional so workflows
                # that omit it still validate — ComfyUI rejects a prompt that
                # omits any `required` input. When omitted it defaults to
                # "none", identical to unquantized behavior and filename, so
                # existing cached .mlpackages still resolve.
                "quantize_nbits": (
                    _discover("list_quant_modes", ["none", "8", "6", "4"]),
                    {"default": "none"},
                ),
                "lora_params": ("LORA_PARAMS",),
            },
        }
@@ -277,20 +255,18 @@ class CoreMLConverter(COREML_NODE):
    def convert(
        self,
        ckpt_name,
        model_version,
        height,
        width,
        batch_size,
        attention_implementation,
        compute_unit,
        controlnet_support,
        quantize_nbits="none",
        lora_params=None,
    ):
-        """Converts a checkpoint's UNet to Core ML.
+        """Converts a LCM model to Core ML.
        Args:
            ckpt_name (str): Checkpoint to convert; its model version is
                auto-detected from the weights.
            height (int): Height of the target image.
            width (int): Width of the target image.
            batch_size (int): Batch size.
@@ -300,8 +276,10 @@ class CoreMLConverter(COREML_NODE):
            coreml_model: The converted Core ML model.
        The converted model is also saved to "models/unet" directory and
-        can be loaded with the "Load Core ML UNet" node.
+        can be loaded with the "LCMCoreMLLoaderUNet" node.
        """
        model_version = ModelVersion[model_version]
        lora_params = lora_params or {}
        lora_params = [(k, v[0]) for k, v in lora_params.items()]
        lora_params = sorted(lora_params, key=lambda lora: lora[0])
@@ -310,19 +288,24 @@ class CoreMLConverter(COREML_NODE):
        h = height
        w = width
        sample_size = (h // 8, w // 8)
-        import coreml_diffusion
+        batch_size = batch_size
-
+        cn_support_str = "_cn" if controlnet_support else ""
-        out_name = coreml_diffusion.compose_out_name(
+        lora_str = (
-            ckpt_name=ckpt_name,
+            "_" + "_".join(lora_param[0].split(".")[0] for lora_param in lora_params)
-            batch_size=batch_size,
+            if lora_params
-            width=w,
+            else ""
            height=h,
            controlnet_support=controlnet_support,
            attention_implementation=attention_implementation,
            lora_names=coreml_diffusion.lora_names_from_params(lora_params),
            quantize_nbits=quantize_nbits,
        )
        attn_str = (
            "_"
            + {"SPLIT_EINSUM": "se", "SPLIT_EINSUM_V2": "se2", "ORIGINAL": "orig"}[
                attention_implementation
            ]
        )
        out_name = f"{ckpt_name.split('.')[0]}{lora_str}_{batch_size}x{w}x{h}{cn_support_str}{attn_str}"
        out_name = out_name.replace(" ", "_")
        logger.info(f"Converting {ckpt_name} to {out_name}")
        logger.info(f"Batch size: {batch_size}")
        logger.info(f"Width: {w}, Height: {h}")
@@ -330,14 +313,11 @@ class CoreMLConverter(COREML_NODE):
        logger.info(f"Attention implementation: {attention_implementation}")
        if lora_params:
-            logger.info("LoRAs used:")
+            logger.info(f"LoRAs used:")
            for lora_param in lora_params:
                logger.info(f"  {lora_param[0]} - strength: {lora_param[1]}")
-        # Resolve the ComfyUI models/unet path here (a node concern); the package
+        unet_out_path = converter.get_out_path("unet", f"{out_name}")
        # takes the output path as an injected argument.
        unet_path = folder_paths.get_folder_paths("unet")[0]
        unet_out_path = os.path.join(unet_path, f"{out_name}_unet.mlpackage")
        ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
        config_filename = ckpt_name.split(".")[0] + ".yaml"
@@ -345,19 +325,22 @@ class CoreMLConverter(COREML_NODE):
        if config_path:
            logger.info(f"Using config file {config_path}")
-        coreml_diffusion.convert(
+        converter.convert(
-            ckpt_path,
+            ckpt_path=ckpt_path,
-            None,  # model_version auto-detected from the checkpoint
+            model_version=model_version,
-            unet_out_path,
+            unet_out_path=unet_out_path,
            sample_size=sample_size,
            batch_size=batch_size,
            controlnet_support=controlnet_support,
            lora_weights=lora_weights,
            attn_impl=attention_implementation,
            config_path=config_path,
            quantize_nbits=quantize_nbits,
        )
-        return (CoreMLModel(unet_out_path, compute_unit),)
+        unet_target_path = converter.compile_model(
            out_path=unet_out_path, out_name=out_name, submodule_name="unet"
        )
        return (CoreMLModel(unet_target_path, compute_unit, "compiled"),)
    @staticmethod
    def lora_path(lora_name):
@@ -1,63 +0,0 @@
 [build-system]
 requires = ["hatchling"]
 build-backend = "hatchling.build"
 [project]
 name = "comfyui-coremlsuite"
 description = "This extension contains a set of custom nodes for ComfyUI that allow you to use Core ML models in your ComfyUI workflows."
 version = "2.1.2"
 license = "MIT"
 requires-python = ">=3.12"
 dependencies = [
    # torch is provided by the host (ComfyUI) and intentionally left unpinned
    # here: a hard torch cap would downgrade the host's torch and break its
    # torchvision/torchaudio ABI. coreml-diffusion pulls torch>=2.7 transitively.
    # >=0.1.6: model-version auto-detection (convert(model_version=None)) and the
    # dropped <3.13 Python cap (kept in sync with this package's requires-python).
    "coreml-diffusion>=0.1.6,<0.2",
    "coremltools>=9,<10",
    "numpy>=2,<3",
 ]
 [project.urls]
 Repository = "https://github.com/aszc-dev/ComfyUI-CoreMLSuite"
 [tool.hatch.build.targets.wheel]
 packages = ["coreml_suite"]
 [tool.comfy]
 PublisherId = "aszc-dev"
 DisplayName = "ComfyUI-CoreMLSuite"
 Icon = "https://raw.githubusercontent.com/aszc-dev/ComfyUI-CoreMLSuite/main/assets/snake.png"
 requires-comfyui = ">=0.3.27"
 [dependency-groups]
 dev = [
    "pillow>=12.2.0",
    "psutil>=7.2.2",
    "pytest>=9.0.3",
 ]
 comfy = [
    "comfyui-frontend-package==1.14.6",
    "torchvision",
    "torchaudio",
    "torchsde",
    "einops",
    "tokenizers>=0.13.3",
    "safetensors>=0.4.2",
    "aiohttp>=3.11.8",
    "yarl>=1.18.0",
    "kornia>=0.7.1",
    "spandrel",
    "soundfile",
    "sentencepiece",
 ]
 [tool.pytest.ini_options]
 markers = [
    "unit: framework-free unit test (Tier 0)",
    "smoke: macOS-ARM smoke test on a synthetic micro-model (Tier 1)",
    "m2: requires Apple Silicon + Neural Engine (Tier 2)",
 ]
 testpaths = ["tests"]
 addopts = ["--import-mode=importlib", "--confcutdir=tests"]
@@ -1,4 +1,6 @@
-coreml-diffusion>=0.1.4,<0.2
+git+https://github.com/apple/ml-stable-diffusion.git
-coremltools>=9,<10
+coremltools>=7.1
-numpy>=2,<3
+overrides
-diffusers>=0.30
+diffusers>=0.22
 peft>=0.6.2
 omegaconf>=2.3
@@ -1,208 +0,0 @@
 # Conversion Extraction — Seam Inventory (`docs/extraction/seam.md`)
 > **Gate E0 deliverable.** Symbol-by-symbol cut line between the future `coreml_diffusion`
 > package (CONVERSION) and what stays in `coreml_suite` (the ComfyUI side).
 >
 > **Confidence legend:**
 > - ✅ **verified** — read directly from the current source in this repo.
 > - 🔍 **confirm** — inferred / partially seen; Claude Code must `grep`-verify before acting.
 >
 > **Cut rule:** a symbol goes to `coreml_diffusion` iff it participates in producing the `.mlpackage`
 > artifact AND can be made free of `comfy` / `folder_paths` / `comfy_extras`. The runtime
 > *loader* that **runs** a compiled model stays in the suite.
 ---
 ## 1. File-level map
 | File | Side | Status | Note |
 |---|---|---|---|
 | `coreml_suite/model_version.py` | **coreml_diffusion** | ✅ | Already `Enum`-only, zero comfy. Becomes pkg source of truth. |
 | `coreml_suite/attention.py` | **coreml_diffusion** | ✅ | `ATTENTION_IMPLEMENTATIONS` tuple; pure constant. |
 | `coreml_suite/core/naming.py` | **coreml_diffusion** | ✅ | `compose_out_name` = cache-key contract. Move (not copy). |
 | `coreml_suite/converter.py` | **coreml_diffusion** (mostly) | ✅ | Main conversion. One symbol stays-adjacent: `get_out_path` (folder_paths) is replaced by injected `out_path`. |
 | `coreml_suite/conversion/attention.py` | **coreml_diffusion** | ✅ | `apply_attention_implementation`. Imports `logging`,`torch` only — no comfy. |
 | `coreml_suite/conversion/shapes.py` | **coreml_diffusion** | ✅ | `conv2d_output_shape`. Pure math, no imports. |
 | `coreml_suite/conversion/trace.py` | **coreml_diffusion** | ✅ | Imports `types.MethodType`, `diffusers...Transformer2DModel` only — torch/diffusers. |
 | `coreml_suite/conversion/unet.py` | **coreml_diffusion** | ✅ | `CoreMLUNetWrapper`. Imports `torch` only — no comfy. |
 | `coreml_suite/lcm/converter.py` | **coreml_diffusion** (after dedup) | ✅ | Dup helpers deleted; `MODEL_VERSION` HF-hardcode (L22) → E-LCM. `folder_paths` (L111) + `comfy.model_management` (L54) confirmed present → CUT. |
 | `coreml_suite/lcm/unet.py` | **coreml_diffusion** | ✅ | `UNet2DConditionModelLCM(UNet2DConditionModel)`. diffusers-only, no comfy. |
 | `coreml_suite/config.py` | **STAYS** | ✅ | Imports `comfy.supported_models_base`/`latent_formats`/`model_detection`. **Inference-side** (`get_model_config`), NOT conversion. |
 | `coreml_suite/coreml_model.py` | **STAYS** | ✅ | `CoreMLModel` = runtime loader (runs `.mlpackage`). Desktop/Python inference; not used on iOS. |
 | `coreml_suite/nodes.py` | **STAYS** | ✅ | Nodes; will call `coreml_diffusion` + own `folder_paths` path resolution + discovery dropdowns. |
 | `coreml_suite/lcm/nodes.py` | **STAYS** | ✅ | `COREML_CONVERT_LCM` node. |
 | `coreml_suite/models.py` | **STAYS** | ✅ | Inference: `add_sdxl_model_options`, `is_sdxl`, `get_model_patcher`, `get_latent_image`. |
 | `coreml_suite/latents.py` | **STAYS** | ✅ | Inference chunking (MODERNIZATION Phase 3 target, not this spec). |
 | `coreml_suite/controlnet.py` | **STAYS** | ✅ | Inference-side controlnet. Distinct from converter `add_cnet_support`. |
 | `coreml_suite/lcm/utils.py` | **STAYS** | ✅ | `add_lcm_model_options`, `lcm_patch`, `is_lcm`; imports `comfy_extras`. Inference. |
 | `coreml_suite/logger.py` | **both / copy** | ✅ | Trivial. Package gets its own logger; suite keeps its. |
 ---
 ## 2. Symbol-level: `coreml_suite/converter.py` (main conversion)
 | Symbol | Side | Status | Cut action |
 |---|---|---|---|
 | `DEFAULT_TRACE_TIMESTEP`, `TEXT_TOKEN_SEQUENCE_LENGTH` | coreml_diffusion | ✅ | Move as-is (module constants). |
 | `get_unet(model_version, ref_unet, attention_implementation)` | coreml_diffusion | ✅ | Move. Uses `conversion.{trace,attention,unet}`. No comfy. |
 | `get_encoder_hidden_states_shape(ref_unet, batch_size)` | coreml_diffusion | ✅ | Move. Reads `ref_unet.config.cross_attention_dim`. Pure. |
 | `get_coreml_inputs(sample_inputs)` | coreml_diffusion | ✅ | Move. `ct.TensorType` build. |
 | `load_coreml_model(out_path)` | coreml_diffusion | ✅ | Move. `ct.models.MLModel(out_path)`. (Dedup target vs LCM copy.) |
 | `convert_to_coreml(submodule, ts_module, inputs, names, out_path)` | coreml_diffusion | ✅ | Move. `ct.convert(...)`. (Dedup target vs LCM copy.) |
 | `get_sample_input(batch, ehs_shape, sample_shape)` | coreml_diffusion | ✅ | Move. **Merge** with LCM variant (LCM passes extra `scheduler` → optional param). |
 | `lcm_inputs(sample_unet_inputs)` | coreml_diffusion | ✅ | Move. Adds `timestep_cond`. |
 | `sdxl_inputs(sample_unet_inputs, ref_unet, model_version)` | coreml_diffusion | ✅ | Move. `time_ids`/`text_embeds`/`add_embeds`. |
 | `add_cnet_support(sample_shape, ref_unet)` | coreml_diffusion | ✅ | Move. Builds `additional_residual_*` inputs from unet block channels. |
 | `convert_unet(ref_unet, model_version, unet_out_path, ...)` | coreml_diffusion | ✅ | Move. Orchestrates trace→convert→**quant (palettize)**→save. Quant travels here (E6). |
 | `convert(ckpt_path, model_version, unet_out_path, ...)` | coreml_diffusion | ✅ | Move. **Make kw-only past `ckpt_path,model_version,out_path`** (contract). Validates `attn_impl`. |
 | `load_unet(ckpt_path, config_path)` | coreml_diffusion | ✅ | Move. `UNet2DConditionModel.from_single_file`. |
 | `get_out_path(submodule_name, model_name)` | **STAYS (node)** | ✅ | Uses `folder_paths.get_folder_paths`. **Delete from converter; node resolves path and passes `out_path` in.** |
 **Apple `python_coreml_stable_diffusion` footprint on this path:** ✅ **none.** Verified by grep:
 zero imports in `converter.py` / `conversion/*`. Main path uses `diffusers` +
 local `CoreMLUNetWrapper`. (And the runtime `CoreMLModel` is now a local coremltools wrapper too —
 see §6 stale-spec note.)
 ---
 ## 3. Symbol-level: `coreml_suite/lcm/converter.py` (LCM — dedup + defer)
 | Symbol | Side | Status | Cut action |
 |---|---|---|---|
 | `load_coreml_model` (LCM copy) | DELETE | ✅ | Duplicate of main. Remove; use `coreml_diffusion.load_coreml_model`. |
 | `convert_to_coreml` (LCM copy) | DELETE | ✅ | Duplicate of main. Remove. |
 | `get_out_path` (LCM copy, folder_paths) | DELETE | ✅ | Duplicate + comfy. Remove; node injects `out_path`. |
 | `get_sample_input(..., scheduler)` (LCM copy) | MERGE → coreml_diffusion | ✅ | Fold `scheduler` into shared `get_sample_input` as optional param. |
 | `MODEL_NAME` (= LCM_Dreamshaper) | **E-LCM** | ✅ | HF hardcode. Removing it is the behavior change → E-LCM, not E2. |
 | `convert(out_path, sample_size, batch_size, controlnet_support)` (LCM, L190) | coreml_diffusion (via unified) | ✅ | Route through `coreml_diffusion.convert(model_version=LCM, ...)` in E-LCM. |
 | `from comfy.model_management import get_torch_device` (L54, in `get_scheduler`) | **CUT** | ✅ | Confirmed present. Inject `device`. |
 | module-global attention set at import | n/a | ✅ | **No module global.** Attention already per-call: `get_unets` (L36) calls `apply_attention_implementation(ref_unet, "SPLIT_EINSUM")`. No `ATTENTION_IMPLEMENTATION_IN_EFFECT` anywhere in repo. (Note: LCM hardcodes `"SPLIT_EINSUM"` — pass `attn_impl` through in dedup.) |
 ---
 ## 4. Symbol-level: `coreml_suite/core/naming.py` → `coreml_diffusion/naming.py`
 | Symbol | Side | Status | Cut action |
 |---|---|---|---|
 | `compose_out_name(...)` | coreml_diffusion | ✅ | **Move** (cache-key contract). Node imports from pkg. |
 | `lora_names_from_params(...)` | coreml_diffusion | ✅ | Move. |
 | `ATTN_SUFFIX` dict | coreml_diffusion | ✅ | Move. |
 | `QUANT_NBITS_VALUES` | coreml_diffusion | ✅ | Move; backs `list_quant_modes()`. |
 | `tests/unit/test_characterization_out_name.py` | re-point | ✅ | Change import to `coreml_diffusion.naming`. Assertions/values **unchanged**. |
 ---
 ## 5. Discovery API + status registry (new in `coreml_diffusion/__init__.py`)
 ```python
 from enum import Enum
 class Status(Enum):
    VERIFIED = "verified"          # has a golden anchor + passing [M2-ANE] check
    EXPERIMENTAL = "experimental"  # convertible, not yet anchored/verified
 # Single source of truth. Suite gates on this, NOT on a hardcoded node list.
 # KEY by ModelVersion enum MEMBER (not a bare string) so list_model_versions can
 # emit .name — see the .name decision below. Keying by the lowercase .value string
 # (as an earlier draft of this block did) returns ["sd15",...], which the node then
 # reverses via ModelVersion[...] → KeyError. Do NOT key by .value.
 _MODEL_STATUS = {
    ModelVersion.SD15:         Status.VERIFIED,
    ModelVersion.SDXL:         Status.VERIFIED,
    ModelVersion.SDXL_REFINER: Status.EXPERIMENTAL,   # → VERIFIED after a refiner golden anchor
    ModelVersion.LCM:          Status.EXPERIMENTAL,   # → VERIFIED after E-LCM golden anchor
 }
 def list_model_versions(include_experimental: bool = False) -> list[str]:
    return [v.name for v, s in _MODEL_STATUS.items()   # .name → "SD15","SDXL" (see decision)
            if s is Status.VERIFIED or (include_experimental and s is Status.EXPERIMENTAL)]
 def list_attention_impls() -> list[str]:   # from attention.ATTENTION_IMPLEMENTATIONS
    ...
 def list_quant_modes() -> list[str]:       # from naming.QUANT_NBITS_VALUES
    ...
 CONTRACT_VERSION = "1.0"
 # Additive-only: adding an id or promoting EXPERIMENTAL→VERIFIED = minor bump (Suite unaffected).
 # Removing/renaming an id, or demoting VERIFIED→EXPERIMENTAL = MAJOR bump + migration note.
 ```
 **Decision check (`.name` vs `.value`): RESOLVED → `.name`.** ✅
 Verified in current source:
 - Node renders `ModelVersion.SD15.name` / `ModelVersion.SDXL.name` → `"SD15"`, `"SDXL"`
  (`nodes.py:224-225`).
 - Node reverses the dropdown string with `model_version = ModelVersion[model_version]`
  (`nodes.py:286`) — i.e. **lookup by NAME**. Feeding it a `.value` (`"sd15"`) raises `KeyError`.
 - Enum values are lowercase (`model_version.py`: `SD15="sd15"`, `SDXL="sdxl"`,
  `SDXL_REFINER="sdxl_refiner"`, `LCM="lcm"`).
 - `compose_out_name` does NOT consume the model_version string (grep of `core/naming.py` empty) —
  no coupling there, so no constraint from that side.
 **Decision:** `list_model_versions()` returns `.name` (uppercase). Saved workflows store `"SD15"`,
 node already validates them via `ModelVersion[...]`. The `_MODEL_STATUS` block above was corrected
 to key by enum member and emit `.name`. **The earlier `v.value` form was a latent bug.**
 ---
 ## 6. `python_coreml_stable_diffusion` split (Gate E0 line to fill by grep)
 | Use | Side | Status |
 |---|---|---|
 | `coreml_model.CoreMLModel` (runs compiled model) | **STAYS** (suite runtime) | ✅ — **local class**, not Apple's |
 | `unet.UNet2DConditionModel*` internals | **gone** — `converter.py:319` uses `diffusers.UNet2DConditionModel.from_single_file` | ✅ |
 | `AttentionImplementations` enum | gone — local `apply_attention_implementation` + `attention.py` tuple | ✅ |
 | `calculate_conv2d_output_shape` | gone — replaced by `conversion/shapes.conv2d_output_shape` | ✅ |
 > ### ⚠️ SPEC IS STALE: `ml-stable-diffusion` is already fully removed
 > Commit #58 ("replace apple/ml-stable-diffusion with native diffusers conversion") already did
 > the de-Apple work. Verified now:
 > - **Zero** `python_coreml_stable_diffusion` runtime imports anywhere in `coreml_suite` (only a
 >   docstring mention at `core/__init__.py:4`).
 > - `coreml_suite/coreml_model.py:8` `CoreMLModel` is a **local** wrapper over
 >   `coremltools.models.MLModel` (`coreml_model.py:22`) — it does **not** import Apple's class.
 > - `ml-stable-diffusion` / `python_coreml_stable_diffusion` appears in **neither** `pyproject.toml`
 >   **nor** `requirements.txt`. It is not a dependency at all.
 >
 > **Consequences for the spec (correct these in CONVERTER_EXTRACTION_SPEC.md):**
 > - §0.3 premise ("runtime loader = `python_coreml_stable_diffusion.coreml_model.CoreMLModel`,
 >   stays in suite") is **wrong**: the loader is already the local `coreml_model.CoreMLModel`. The
 >   "stays in suite" conclusion still holds; the identity does not.
 > - **Gate E0 item "ml-stable-diffusion pinned SHA — BLOCKER if unpinned" is MOOT** — there is no
 >   such dep to pin. Mark it N/A, not BLOCKER.
 > - **E4/E5 dependency lists must drop `git+...ml-stable-diffusion@<sha>`.** Package runtime deps
 >   are: `coremltools`, `diffusers`, `peft` (LoRA), `omegaconf` (config), `numpy`, `torch`. Confirm
 >   `peft`/`omegaconf` actually used before listing (grep at E4).
 > - The "keep `python_coreml_stable_diffusion` as a suite dep for the loader" instruction in E5 is
 >   **void** — coremltools backs the loader.
 ---
 ## 7. Pre-flight checklist before E1 (run these greps)
 ```
 grep -rn "import comfy"            coreml_suite/conversion coreml_suite/converter.py coreml_suite/lcm/converter.py coreml_suite/lcm/unet.py
 grep -rn "folder_paths"            coreml_suite/converter.py coreml_suite/lcm/converter.py
 grep -rn "model_management"        coreml_suite/lcm
 grep -rn "python_coreml_stable_diffusion" coreml_suite
 grep -rn "ATTENTION_IMPLEMENTATION_IN_EFFECT" coreml_suite
 grep -rn "SimianLuo\|LCM_Dreamshaper" coreml_suite/lcm
 ```
 Every 🔍 above resolves to ✅ or a correction once these run. Do not start moving code (E2)
 with any 🔍 unresolved on the CONVERSION side.
 **STATUS (run 2026-05-26): all 🔍 resolved.** Summary of what the greps found:
 - `conversion/*`, `lcm/unet.py`: comfy-free (torch/diffusers only). ✅
 - `converter.py`: only comfy reach-in is `folder_paths` in `get_out_path` (L91-94) → inject `out_path`.
 - `lcm/converter.py`: `folder_paths` (L111-114) + `comfy.model_management.get_torch_device` (L54)
  → cut both. Dup helpers (`load_coreml_model`,`convert_to_coreml`,`get_out_path`,`get_sample_input`)
  confirmed → dedup E2. `MODEL_VERSION="SimianLuo/LCM_Dreamshaper_v7"` (L22) → E-LCM.
 - No attention module-global anywhere (`ATTENTION_IMPLEMENTATION_IN_EFFECT` absent); already per-call.
  LCM hardcodes `"SPLIT_EINSUM"` in `get_unets` — thread `attn_impl` through during dedup.
 - `.name` vs `.value`: **decided `.name`** (node reverses via `ModelVersion[...]`). §5 corrected.
 - `ml-stable-diffusion`: **already gone** (#58). §6 stale-spec note added — fix the spec's E0/E4/E5
  dep + pinning items.
 Two grep blind-spots to note (the checklist above doesn't cover them, but cheap to add): the
 `folder_paths` grep only scans the two converter files — also grep `coreml_suite/lcm/utils.py`
 (it imports `comfy.model_management` at L3, but it's inference/STAYS, so fine) and confirm no other
 `conversion/` file grew a comfy import since.
@@ -1,61 +0,0 @@
 """Pytest bootstrap for ComfyUI-CoreMLSuite tests.
 - Adds the ComfyUI checkout to sys.path so the framework-coupled modules
  that transitively import `comfy.*` resolve when pytest is invoked from
  this package's root.
 - Auto-applies tier markers based on the directory a test lives in, so
  individual files don't have to repeat @pytest.mark.unit / .smoke.
 """
 import sys
 from pathlib import Path
 import pytest
 REPO_ROOT = Path(__file__).resolve().parents[1]
 COMFY_DIR = REPO_ROOT.parents[1]
 for p in (str(COMFY_DIR), str(REPO_ROOT)):
    if p not in sys.path:
        sys.path.insert(0, p)
 _TIER_BY_DIR = {
    "tests/unit": "unit",
    "tests/m2": "m2",
    "tests/integration": "m2",
    "tests/smoke": "smoke",
 }
 # When the user asks for a single tier (-m unit / -m smoke), skip the other
 # directories at collection time. Tier-0 cannot afford to import tests/smoke
 # files because they pull in coremltools which Linux CI won't have.
 _TIER_DIRS = {
    "unit": ("/tests/unit/",),
    "m2": ("/tests/m2/", "/tests/integration/"),
    "smoke": ("/tests/smoke/",),
 }
 def pytest_ignore_collect(collection_path, config):
    expr = config.option.markexpr
    if expr not in _TIER_DIRS:
        return None
    allowed = _TIER_DIRS[expr]
    rel = str(collection_path).replace("\\", "/")
    if "/tests/" not in rel:
        return None
    # Always allow tests/ root + the tier's own dirs.
    if rel.endswith("/tests"):
        return None
    if any(frag in rel + "/" for frag in allowed):
        return None
    return True
 def pytest_collection_modifyitems(config, items):
    for item in items:
        path = str(item.fspath).replace("\\", "/")
        for fragment, marker in _TIER_BY_DIR.items():
            if f"/{fragment}/" in path:
                item.add_marker(getattr(pytest.mark, marker))
                break
@@ -0,0 +1,72 @@
 import json
 import os
 import pytest
 import requests
 from PIL import Image
 import numpy as np
 from folder_paths import get_save_image_path, get_output_directory
 IMAGE_PREFIX = "E2E-1.5-CoreML"
 class OutputImageRepository:
    def __init__(self, name_prefix):
        self.name_prefix = name_prefix
    def list_images(self):
        full_output_folder, _, _, _, _ = get_save_image_path(
            self.name_prefix, get_output_directory(), 512, 512
        )
        return full_output_folder, os.listdir(full_output_folder)
    def delete_images(self):
        full_output_folder, images = self.list_images()
        for image in images:
            os.remove(os.path.join(full_output_folder, image))
@pytest.fixture(scope="module")
 def output_image_repository():
    repo = OutputImageRepository(IMAGE_PREFIX)
    yield repo
    repo.delete_images()
 def test_basic_conversion_1_5(output_image_repository):
    with open("integration/workflows/e2e-1.5-basic-conversion.json") as f:
        prompt = json.load(f)
    queue_prompt(prompt)
    full_output_folder, images = output_image_repository.list_images()
    assert len(images) == 2
    assert all(image.startswith(IMAGE_PREFIX) for image in images)
    assert all(image.endswith(".png") for image in images)
    assert all(
        os.path.isfile(os.path.join(full_output_folder, image)) for image in images
    )
    image1 = Image.open(os.path.join(full_output_folder, images[0]))
    image2 = Image.open(os.path.join(full_output_folder, images[1]))
    assert psnr(np.array(image1), np.array(image2)) > 30
    assert psnr(np.array(image2), np.array(image1)) > 30
 def psnr(img1, img2):
    mse = np.mean((img1 - img2) ** 2)
    if mse == 0:
        return 100
    PIXEL_MAX = 255.0
    return 20 * np.log10(PIXEL_MAX / np.sqrt(mse))
 def queue_prompt(prompt: dict):
    p = {"prompt": prompt}
    data = json.dumps(p).encode("utf-8")
    req = requests.post("http://localhost:8188/prompt", data=data)
    assert req.status_code == 200
    while True:
        req = requests.get("http://localhost:8188/prompt")
        if req.json()["exec_info"]["queue_remaining"] == 0:
            break
@@ -107,6 +107,7 @@
  "10": {
    "inputs": {
      "ckpt_name": "dreamshaper_8.safetensors",
      "model_version": "SD15",
      "height": 512,
      "width": 512,
      "batch_size": 1,
@@ -178,4 +179,4 @@
      "title": "Save Image"
    }
  }
-}
+}
@@ -1 +0,0 @@
 e89344e544d4edfbd3ebe9a1c78dadb2729f53549666052b74ac7308f326f4fc
@@ -1,170 +0,0 @@
 """[M2-ANE] golden-image anchor.
 Runs the e2e SD1.5 + CoreML workflow against a local ComfyUI server, fetches
 the generated PNG, and asserts both:
  - byte-identical SHA256 against the stored golden, OR
  - PSNR >= GOLDEN_PSNR_MIN_DB against the stored golden PNG.
 The hash is the strict gate (a refactor that doesn't touch the math
 should hit it). PSNR is the soft gate that tolerates the drift a
 toolchain bump injects through different MIL graphs / kernel selection
 / fp accumulation order — anything below the threshold is treated as a
 regression.
 The 20 dB default absorbs Apple Neural Engine run-to-run nondeterminism:
 the same model and seed can drift several dB between runs as the 20
 sampling steps amplify tiny per-step UNet differences (kernel selection /
 fp accumulation order). Same-scene ANE outputs have been observed at
 ~23 dB, so 20 leaves margin while still catching gross regressions — a
 broken image lands far lower. Bump it up for pure-refactor PRs that must
 not change math; down for toolchain bumps.
 Skips entirely on non-Apple-Silicon hosts or when the server / converted
 model is missing, so the unit lane on Linux still passes.
 The first run with no golden writes one and fails so it's reviewed before
 being committed.
 """
 import hashlib
 import json
 import os
 import platform
 import shutil
 import time
 import urllib.error
 import urllib.request
 from pathlib import Path
 import numpy as np
 import pytest
 from PIL import Image
 REPO_ROOT = Path(__file__).resolve().parents[2]
 COMFY_DIR = Path(os.environ.get("COMFY_DIR", REPO_ROOT.parents[1])).resolve()
 COMFY_HOST = os.environ.get("COMFY_HOST", "localhost")
 COMFY_PORT = int(os.environ.get("COMFY_PORT", "8188"))
 COMFY_URL = f"http://{COMFY_HOST}:{COMFY_PORT}"
 CKPT_NAME = os.environ.get("CKPT_NAME", "v1-5-pruned-emaonly.safetensors")
 WORKFLOW_PATH = (
    REPO_ROOT / "tests" / "integration" / "workflows" / "e2e-1.5-basic-conversion.json"
 )
 GOLDEN_DIR = Path(__file__).parent / "goldens"
 GOLDEN_HASH_PATH = GOLDEN_DIR / "sd15_seed42.sha256"
 GOLDEN_PNG_PATH = GOLDEN_DIR / "sd15_seed42.png"
 GOLDEN_PSNR_MIN_DB = float(os.environ.get("GOLDEN_PSNR_MIN_DB", "20"))
 SEED = 42
 def _server_reachable() -> bool:
    try:
        with urllib.request.urlopen(f"{COMFY_URL}/prompt", timeout=3) as r:
            return r.status == 200
    except (urllib.error.URLError, urllib.error.HTTPError, ConnectionError):
        return False
@pytest.fixture(scope="module")
 def comfy_server():
    if platform.machine() != "arm64":
        pytest.skip("requires Apple Silicon")
    if not _server_reachable():
        pytest.skip(f"ComfyUI server not reachable at {COMFY_URL}")
    return COMFY_URL
 def _http_post_json(path: str, payload: dict) -> dict:
    data = json.dumps(payload).encode("utf-8")
    req = urllib.request.Request(
        f"{COMFY_URL}{path}", data=data,
        headers={"Content-Type": "application/json"}, method="POST",
    )
    with urllib.request.urlopen(req, timeout=300) as r:
        return json.loads(r.read().decode())
 def _http_get_json(path: str, timeout: int = 300) -> dict:
    """ComfyUI runs UNet inference on its single asyncio loop, so GET /prompt
    blocks while the queued prompt is executing. Use a generous timeout."""
    with urllib.request.urlopen(f"{COMFY_URL}{path}", timeout=timeout) as r:
        return json.loads(r.read().decode())
 def _drain_queue(timeout_s: int = 600) -> None:
    deadline = time.time() + timeout_s
    while time.time() < deadline:
        try:
            q = _http_get_json("/prompt")
        except (urllib.error.URLError, TimeoutError):
            # Transient block while server executes; retry until our overall
            # deadline expires.
            continue
        if q.get("exec_info", {}).get("queue_remaining", -1) == 0:
            return
        time.sleep(2)
    raise TimeoutError(f"queue did not drain within {timeout_s}s")
 def _post_workflow_and_collect_png() -> bytes:
    workflow = json.loads(WORKFLOW_PATH.read_text())
    for nid in ("4", "10"):
        if nid in workflow:
            workflow[nid]["inputs"]["ckpt_name"] = CKPT_NAME
    for nid in ("3", "11"):
        if nid in workflow and "seed" in workflow[nid].get("inputs", {}):
            workflow[nid]["inputs"]["seed"] = SEED
    # Drop the MPS reference branch — only the Core ML pipeline is needed here.
    for nid in ("3", "8", "9"):
        workflow.pop(nid, None)
    _http_post_json("/prompt", {"prompt": workflow})
    _drain_queue()
    comfy_out = COMFY_DIR / "output"
    matches = sorted(comfy_out.glob("E2E-1.5-CoreML_*.png"), reverse=True)
    if not matches:
        raise FileNotFoundError(f"no Core ML image under {comfy_out}")
    return matches[0].read_bytes()
 def _psnr(a: np.ndarray, b: np.ndarray) -> float:
    mse = float(np.mean((a.astype(np.float64) - b.astype(np.float64)) ** 2))
    if mse == 0:
        return 100.0
    return 20.0 * float(np.log10(255.0 / np.sqrt(mse)))
 def test_sd15_seed42_image_matches_golden(comfy_server):
    GOLDEN_DIR.mkdir(parents=True, exist_ok=True)
    png_bytes = _post_workflow_and_collect_png()
    sha = hashlib.sha256(png_bytes).hexdigest()
    if not GOLDEN_HASH_PATH.exists() or not GOLDEN_PNG_PATH.exists():
        GOLDEN_HASH_PATH.write_text(sha + "\n")
        # Persist the PNG too for visual diffing + PSNR.
        tmp_path = Path(__file__).parent / "_latest_generated.png"
        tmp_path.write_bytes(png_bytes)
        shutil.copy2(tmp_path, GOLDEN_PNG_PATH)
        pytest.fail(
            f"No golden present; wrote {GOLDEN_HASH_PATH.name} and "
            f"{GOLDEN_PNG_PATH.name}. Review the image and re-run."
        )
    expected_hash = GOLDEN_HASH_PATH.read_text().strip()
    if sha == expected_hash:
        return
    # Hash drift: fall back to PSNR to distinguish a refactor-safe rounding
    # change from a real regression.
    a = np.array(Image.open(GOLDEN_PNG_PATH).convert("RGB"))
    b_path = Path(__file__).parent / "_latest_generated.png"
    b_path.write_bytes(png_bytes)
    b = np.array(Image.open(b_path).convert("RGB"))
    if a.shape != b.shape:
        pytest.fail(f"shape mismatch: golden={a.shape} actual={b.shape}")
    psnr_db = _psnr(a, b)
    assert psnr_db >= GOLDEN_PSNR_MIN_DB, (
        f"hash drifted (got {sha[:12]}.., expected {expected_hash[:12]}..) and "
        f"PSNR {psnr_db:.2f} dB < {GOLDEN_PSNR_MIN_DB} dB threshold; "
        f"diff PNG at {b_path}"
    )
@@ -1,186 +0,0 @@
 """Characterization tests for coreml_suite.controlnet.
 Locks shapes + dtypes + zero-fill behavior of expand_inputs / no_control /
 extract_residual_kwargs / chunk_control. These pure helpers feed the Core ML
 UNet's additional_residual_N inputs; any drift here silently breaks
 ControlNet-based workflows.
 """
 import numpy as np
 import pytest
 import torch
 from coreml_suite.core.controlnet import (
    chunk_control,
    expand_inputs,
    extract_residual_kwargs,
    no_control,
 )
@pytest.fixture(autouse=True)
 def _deterministic_seed():
    torch.manual_seed(0)
    np.random.seed(0)
 SD15_RESIDUAL_SPEC = {
    "additional_residual_0": {"shape": (2, 320, 64, 64)},
    "additional_residual_1": {"shape": (2, 640, 32, 32)},
    "additional_residual_2": {"shape": (2, 1280, 8, 8)},
 }
 NON_RESIDUAL_SPEC = {
    "sample": {"shape": (2, 4, 64, 64)},
    "encoder_hidden_states": {"shape": (2, 77, 768)},
 }
 # ---------- expand_inputs ----------------------------------------------------
 def test_expand_inputs_doubles_singleton_numpy():
    inputs = {"a": np.ones((1, 4), dtype=np.float32)}
    out = expand_inputs(inputs)
    assert out["a"].shape == (2, 4)
    assert np.array_equal(out["a"], np.ones((2, 4)))
 def test_expand_inputs_doubles_singleton_torch():
    inputs = {"a": torch.ones(1, 4)}
    out = expand_inputs(inputs)
    assert out["a"].shape == (2, 4)
    assert torch.equal(out["a"], torch.ones(2, 4))
 def test_expand_inputs_doubles_singleton_list():
    inputs = {"a": [42]}
    out = expand_inputs(inputs)
    assert out["a"] == [42, 42]
 def test_expand_inputs_skips_already_batched():
    """batch > 1 inputs are returned unchanged (same object identity)."""
    arr = np.ones((2, 4), dtype=np.float32)
    tensor = torch.ones(3, 4)
    lst = [1, 2]
    out = expand_inputs({"a": arr, "b": tensor, "c": lst})
    assert out["a"] is arr
    assert out["b"] is tensor
    assert out["c"] is lst
 def test_expand_inputs_preserves_unknown_value_types():
    # Strings/None pass through untouched — locks current permissive contract.
    inputs = {"s": "hello", "none": None, "int": 7}
    out = expand_inputs(inputs)
    assert out == {"s": "hello", "none": None, "int": 7}
 # ---------- no_control -------------------------------------------------------
 def test_no_control_returns_zero_fp16_for_residuals():
    out = no_control({**SD15_RESIDUAL_SPEC, **NON_RESIDUAL_SPEC})
    # Only additional_residual_* keys are produced.
    assert set(out.keys()) == set(SD15_RESIDUAL_SPEC.keys())
    for key, spec in SD15_RESIDUAL_SPEC.items():
        arr = out[key]
        assert arr.shape == spec["shape"]
        assert arr.dtype == np.float16
        assert np.all(arr == 0)
 def test_no_control_returns_empty_when_no_residuals():
    out = no_control(NON_RESIDUAL_SPEC)
    assert out == {}
 # ---------- extract_residual_kwargs -----------------------------------------
 def test_extract_residual_kwargs_empty_when_model_has_no_residual_inputs():
    out = extract_residual_kwargs(NON_RESIDUAL_SPEC, control={"output": [], "middle": []})
    assert out == {}
 def test_extract_residual_kwargs_none_control_returns_no_control_shapes():
    out = extract_residual_kwargs(SD15_RESIDUAL_SPEC, control=None)
    assert set(out.keys()) == set(SD15_RESIDUAL_SPEC.keys())
    for key, spec in SD15_RESIDUAL_SPEC.items():
        assert out[key].shape == spec["shape"]
        assert out[key].dtype == np.float16
        assert np.all(out[key] == 0)
 def test_extract_residual_kwargs_flattens_output_then_middle_and_casts_fp16():
    """output residuals come first (indexed 0..N-1), then middle residuals
    (indexed N..M-1). Values come out of CPU as fp16 numpy arrays."""
    control = {
        "output": [torch.ones(2, 320, 64, 64) * 0.5, torch.ones(2, 640, 32, 32) * 2.0],
        "middle": [torch.ones(2, 1280, 8, 8) * -1.0],
    }
    out = extract_residual_kwargs(SD15_RESIDUAL_SPEC, control)
    assert set(out.keys()) == {"additional_residual_0", "additional_residual_1", "additional_residual_2"}
    assert out["additional_residual_0"].shape == (2, 320, 64, 64)
    assert out["additional_residual_1"].shape == (2, 640, 32, 32)
    assert out["additional_residual_2"].shape == (2, 1280, 8, 8)
    for arr in out.values():
        assert arr.dtype == np.float16
    # Locked order: index 0 == first output residual (0.5), index 2 == middle (-1.0).
    assert np.allclose(out["additional_residual_0"], 0.5)
    assert np.allclose(out["additional_residual_1"], 2.0)
    assert np.allclose(out["additional_residual_2"], -1.0)
 # ---------- chunk_control ----------------------------------------------------
 def test_chunk_control_none_returns_list_of_nones_with_length_target():
    """`no_control` path: when there's no control, you get [None] * target_size
    (NOT [None, None] regardless of target — this is the contract today)."""
    assert chunk_control(None, 1) == [None]
    assert chunk_control(None, 2) == [None, None]
    assert chunk_control(None, 4) == [None, None, None, None]
@pytest.mark.parametrize(
    "batch,target,expected_chunks",
    [(1, 2, 1), (2, 2, 1), (3, 2, 2), (4, 2, 2), (5, 3, 2), (9, 4, 3)],
 )
 def test_chunk_control_shapes_after_chunking(batch, target, expected_chunks):
    cn = {
        "output": [
            torch.randn(batch, 320, 64, 64),
            torch.randn(batch, 640, 32, 32),
        ],
        "middle": [torch.randn(batch, 1280, 8, 8)],
    }
    chunks = chunk_control(cn, target)
    assert len(chunks) == expected_chunks
    for c in chunks:
        assert c["output"][0].shape == (target, 320, 64, 64)
        assert c["output"][1].shape == (target, 640, 32, 32)
        assert c["middle"][0].shape == (target, 1280, 8, 8)
 def test_chunk_control_preserves_keys_order():
    """Output dicts contain exactly {"output", "middle"} in that order."""
    cn = {
        "output": [torch.zeros(2, 4, 4, 4)],
        "middle": [torch.zeros(2, 4, 4, 4)],
    }
    chunks = chunk_control(cn, 2)
    assert list(chunks[0].keys()) == ["output", "middle"]
 def test_chunk_control_zero_pads_remainder():
    """A batch=3, target=2 split puts the third row alongside a zero row."""
    cn = {
        "output": [torch.arange(3 * 4).reshape(3, 1, 2, 2).float()],
        "middle": [torch.arange(3 * 4).reshape(3, 1, 2, 2).float()],
    }
    chunks = chunk_control(cn, 2)
    assert len(chunks) == 2
    last_out = chunks[-1]["output"][0]
    # First row is the original third row; second row is padding zeros.
    assert torch.equal(last_out[0], cn["output"][0][2])
    assert torch.equal(last_out[1], torch.zeros(1, 2, 2))
@@ -1,228 +0,0 @@
 """Characterization tests for coreml_suite.models.CoreMLInputs.
 Locks the shape transforms applied by chunks() and coreml_kwargs() for the
 four model variants the suite supports: SD1.5, LCM (SD1.5 + timestep_cond),
 SDXL base (time_ids len 6), and SDXL refiner (time_ids len 5).
 These contracts feed the Core ML UNet at runtime; if a refactor silently
 re-shapes them, generation breaks.
 """
 import numpy as np
 import pytest
 import torch
 from coreml_suite.core.inputs import CoreMLInputs
@pytest.fixture(autouse=True)
 def _deterministic_seed():
    torch.manual_seed(0)
    np.random.seed(0)
 # ---------- expected_inputs fixtures (mirror real model expectations) -------
 SD15_EXPECTED = {
    "sample": {"shape": (2, 4, 64, 64)},
    "timestep": {"shape": (2,)},
    "encoder_hidden_states": {"shape": (2, 77, 768)},
 }
 SD15_WITH_CN = {
    **SD15_EXPECTED,
    "additional_residual_0": {"shape": (2, 320, 64, 64)},
    "additional_residual_1": {"shape": (2, 640, 32, 32)},
 }
 LCM_EXPECTED = {
    **SD15_EXPECTED,
    "timestep_cond": {"shape": (2, 256)},
 }
 SDXL_BASE_EXPECTED = {
    "sample": {"shape": (2, 4, 128, 128)},
    "timestep": {"shape": (2,)},
    "encoder_hidden_states": {"shape": (2, 77, 2048)},
    "time_ids": {"shape": (2, 6)},
    "text_embeds": {"shape": (2, 1280)},
 }
 SDXL_REFINER_EXPECTED = {
    "sample": {"shape": (2, 4, 128, 128)},
    "timestep": {"shape": (2,)},
    "encoder_hidden_states": {"shape": (2, 77, 1280)},
    "time_ids": {"shape": (2, 5)},
    "text_embeds": {"shape": (2, 1280)},
 }
 def _sd15_inputs(batch=1, with_control=False, with_ts_cond=False):
    x = torch.randn(batch, 4, 64, 64)
    t = torch.full((batch,), 999.0)
    context = torch.randn(batch, 77, 768)
    control = None
    if with_control:
        control = {
            "output": [torch.randn(batch, 320, 64, 64), torch.randn(batch, 640, 32, 32)],
            "middle": [],
        }
    kwargs = {}
    if with_ts_cond:
        kwargs["timestep_cond"] = torch.randn(batch, 256)
    return CoreMLInputs(x, t, context, control, **kwargs)
 def _sdxl_inputs(batch=1, refiner=False):
    x = torch.randn(batch, 4, 128, 128)
    t = torch.full((batch,), 999.0)
    ctx_dim = 1280 if refiner else 2048
    context = torch.randn(batch, 77, ctx_dim)
    time_ids_dim = 5 if refiner else 6
    time_ids = torch.randn(batch, time_ids_dim)
    text_embeds = torch.randn(batch, 1280)
    return CoreMLInputs(
        x, t, context, control=None, time_ids=time_ids, text_embeds=text_embeds
    )
 # ---------- coreml_kwargs ---------------------------------------------------
 def test_coreml_kwargs_sd15_shapes_and_fp16():
    out = _sd15_inputs(batch=1).coreml_kwargs(SD15_EXPECTED)
    assert set(out.keys()) == {"sample", "encoder_hidden_states", "timestep"}
    assert out["sample"].shape == (1, 4, 64, 64)
    assert out["sample"].dtype == np.float16
    # encoder_hidden_states keeps Comfy's native (b, seq, dim) layout.
    assert out["encoder_hidden_states"].shape == (1, 77, 768)
    assert out["encoder_hidden_states"].dtype == np.float16
    assert out["timestep"].shape == (1,)
    assert out["timestep"].dtype == np.float16
 def test_coreml_kwargs_sd15_with_controlnet_emits_residuals():
    inputs = _sd15_inputs(batch=1, with_control=True)
    out = inputs.coreml_kwargs(SD15_WITH_CN)
    assert "additional_residual_0" in out
    assert "additional_residual_1" in out
    assert out["additional_residual_0"].shape == (1, 320, 64, 64)
    assert out["additional_residual_1"].shape == (1, 640, 32, 32)
 def test_coreml_kwargs_sd15_without_controlnet_zero_fills_residuals():
    inputs = _sd15_inputs(batch=1, with_control=False)
    out = inputs.coreml_kwargs(SD15_WITH_CN)
    assert np.all(out["additional_residual_0"] == 0)
    assert np.all(out["additional_residual_1"] == 0)
 def test_coreml_kwargs_lcm_adds_timestep_cond():
    inputs = _sd15_inputs(batch=1, with_ts_cond=True)
    out = inputs.coreml_kwargs(LCM_EXPECTED)
    assert "timestep_cond" in out
    assert out["timestep_cond"].shape == (1, 256)
    assert out["timestep_cond"].dtype == np.float16
 def test_coreml_kwargs_lcm_skips_timestep_cond_when_not_provided():
    """timestep_cond is only forwarded when the input supplied one — even if
    the model's expected_inputs lists it."""
    inputs = _sd15_inputs(batch=1, with_ts_cond=False)
    out = inputs.coreml_kwargs(LCM_EXPECTED)
    assert "timestep_cond" not in out
 def test_coreml_kwargs_sdxl_base_emits_time_ids_and_text_embeds():
    out = _sdxl_inputs(batch=1, refiner=False).coreml_kwargs(SDXL_BASE_EXPECTED)
    assert out["time_ids"].shape == (1, 6)
    assert out["text_embeds"].shape == (1, 1280)
    assert out["time_ids"].dtype == np.float16
    assert out["text_embeds"].dtype == np.float16
 def test_coreml_kwargs_sdxl_refiner_uses_len5_time_ids():
    out = _sdxl_inputs(batch=1, refiner=True).coreml_kwargs(SDXL_REFINER_EXPECTED)
    assert out["time_ids"].shape == (1, 5)
 # ---------- chunks ----------------------------------------------------------
 def test_chunks_sd15_pad_to_batch2_returns_one_chunk():
    chunked = _sd15_inputs(batch=1).chunks(SD15_EXPECTED)
    assert len(chunked) == 1
    c = chunked[0]
    assert c.x.shape == (2, 4, 64, 64)
    assert c.t.shape == (2,)
    # context shape: (b, seq, dim) padded along batch dim.
    assert c.context.shape == (2, 77, 768)
    assert c.control is None
    assert c.ts_cond is None
    assert c.time_ids is None
    assert c.text_embeds is None
 def test_chunks_sd15_with_controlnet_chunks_residuals_too():
    chunked = _sd15_inputs(batch=1, with_control=True).chunks(SD15_EXPECTED)
    assert len(chunked) == 1
    cn = chunked[0].control
    assert cn is not None
    assert cn["output"][0].shape == (2, 320, 64, 64)
    assert cn["output"][1].shape == (2, 640, 32, 32)
 def test_chunks_lcm_carries_timestep_cond_per_chunk():
    chunked = _sd15_inputs(batch=1, with_ts_cond=True).chunks(LCM_EXPECTED)
    assert len(chunked) == 1
    assert chunked[0].ts_cond is not None
    assert chunked[0].ts_cond.shape == (2, 256)
 def test_chunks_sdxl_base_propagates_time_ids_and_text_embeds():
    chunked = _sdxl_inputs(batch=1, refiner=False).chunks(SDXL_BASE_EXPECTED)
    assert len(chunked) == 1
    c = chunked[0]
    assert c.time_ids is not None and c.time_ids.shape == (2, 6)
    assert c.text_embeds is not None and c.text_embeds.shape == (2, 1280)
 def test_chunks_sdxl_refiner_uses_len5_time_ids():
    chunked = _sdxl_inputs(batch=1, refiner=True).chunks(SDXL_REFINER_EXPECTED)
    assert chunked[0].time_ids.shape == (2, 5)
 def test_chunks_sdxl_synthesizes_zero_time_ids_when_caller_omits():
    """If the model expects time_ids but caller passed nothing, the suite
    fabricates a zero-filled tensor. Lock that fallback."""
    x = torch.randn(1, 4, 128, 128)
    t = torch.full((1,), 999.0)
    context = torch.randn(1, 77, 2048)
    inputs = CoreMLInputs(x, t, context, control=None)
    chunked = inputs.chunks(SDXL_BASE_EXPECTED)
    assert chunked[0].time_ids.shape == (2, 6)
    assert torch.equal(chunked[0].time_ids, torch.zeros(2, 6))
    assert chunked[0].text_embeds.shape == (2, 1280)
    assert torch.equal(chunked[0].text_embeds, torch.zeros(2, 1280))
 def test_chunks_splits_batch_into_multiple_target2_chunks():
    """batch=5 with target_batch=2 -> 3 chunks (last padded)."""
    chunked = _sd15_inputs(batch=5).chunks(SD15_EXPECTED)
    assert len(chunked) == 3
    for c in chunked:
        assert c.x.shape == (2, 4, 64, 64)
        assert c.context.shape == (2, 77, 768)
    # Last chunk's second batch row is the zero-pad.
    assert torch.equal(chunked[-1].x[1], torch.zeros(4, 64, 64))
 def test_chunks_timestep_is_broadcast_from_first_value():
    """t is rebuilt from t[0] across all chunks: locks current behavior that
    discards any per-row timestep variation."""
    x = torch.randn(2, 4, 64, 64)
    t = torch.tensor([42.0, 99.0])  # the second value will be lost
    context = torch.randn(2, 77, 768)
    inputs = CoreMLInputs(x, t, context, control=None)
    chunked = inputs.chunks(SD15_EXPECTED)
    assert chunked[0].t.shape == (2,)
    assert torch.equal(chunked[0].t, torch.full((2,), 42.0))
@@ -1,118 +0,0 @@
 """Characterization tests for coreml_suite.latents.
 Locks the *current* behavior of chunk_batch / merge_chunks — including the
 zero-pad regions and the truncation in merge — so a refactor
 cannot silently shift either contract.
 """
 import pytest
 import torch
 from coreml_suite.core.latents import chunk_batch, merge_chunks
@pytest.fixture(autouse=True)
 def _deterministic_seed():
    torch.manual_seed(0)
 def _const_tensor(batch, *rest):
    return torch.arange(batch * 4 * 8 * 8, dtype=torch.float32).reshape(batch, 4, 8, 8)
 # ---------- chunk_batch ------------------------------------------------------
 def test_chunk_batch_passthrough_when_shape_matches():
    x = _const_tensor(2)
    out = chunk_batch(x, (2, 4, 8, 8))
    assert len(out) == 1
    # passthrough: the same object identity is returned (no copy).
    assert out[0] is x
 def test_chunk_batch_pads_single_chunk_when_input_smaller():
    """batch=1, target=2 -> one padded chunk; the second row is exact zero."""
    x = _const_tensor(1)
    out = chunk_batch(x, (2, 4, 8, 8))
    assert len(out) == 1
    assert out[0].shape == (2, 4, 8, 8)
    assert torch.equal(out[0][0], x[0])
    assert torch.equal(out[0][1], torch.zeros(4, 8, 8))
 def test_chunk_batch_splits_exact_multiple():
    """batch=4, target=2 -> two chunks, no padding."""
    x = _const_tensor(4)
    out = chunk_batch(x, (2, 4, 8, 8))
    assert len(out) == 2
    assert out[0].shape == (2, 4, 8, 8)
    assert out[1].shape == (2, 4, 8, 8)
    assert torch.equal(out[0], x[:2])
    assert torch.equal(out[1], x[2:])
 def test_chunk_batch_pads_remainder_chunk():
    """batch=5, target=2 -> chunks=[x[0:2], x[2:4]] then [x[4], 0]."""
    x = _const_tensor(5)
    out = chunk_batch(x, (2, 4, 8, 8))
    assert len(out) == 3
    assert torch.equal(out[0], x[0:2])
    assert torch.equal(out[1], x[2:4])
    last = out[-1]
    assert last.shape == (2, 4, 8, 8)
    assert torch.equal(last[0], x[4])
    # The remainder row is zero-padded; lock that exact contract.
    assert torch.equal(last[1], torch.zeros(4, 8, 8))
    assert last[1].sum() == 0
@pytest.mark.parametrize(
    "batch_size,target,expected_chunks",
    [
        (1, 4, 1),
        (3, 2, 2),
        (5, 3, 2),
        (9, 4, 3),
    ],
 )
 def test_chunk_batch_pad_region_is_zero(batch_size, target, expected_chunks):
    x = _const_tensor(batch_size)
    out = chunk_batch(x, (target, 4, 8, 8))
    assert len(out) == expected_chunks
    mod = batch_size % target
    if mod == 0 and batch_size >= target:
        return
    last = out[-1]
    pad_rows = target - (mod if (mod != 0 and batch_size >= target) else batch_size)
    pad_region = last[-pad_rows:]
    assert torch.equal(pad_region, torch.zeros_like(pad_region))
 # ---------- merge_chunks -----------------------------------------------------
 def test_merge_chunks_exact_concat():
    x = _const_tensor(4)
    chunks = chunk_batch(x, (2, 4, 8, 8))
    merged = merge_chunks(chunks, x.shape)
    assert merged.shape == x.shape
    assert torch.equal(merged, x)
 def test_merge_chunks_truncates_padding():
    """Round-trip with a padded last chunk drops the pad rows."""
    x = _const_tensor(5)
    chunks = chunk_batch(x, (2, 4, 8, 8))
    merged = merge_chunks(chunks, x.shape)
    assert merged.shape == x.shape
    assert torch.equal(merged, x)
 def test_merge_chunks_singleton_returns_equal_copy_when_shape_matches():
    """A singleton chunk list still goes through torch.cat, so we get a new
    tensor equal to the input — locked here because a refactor might be tempted
    to short-circuit and accidentally return the same object."""
    x = _const_tensor(2)
    out = merge_chunks([x], x.shape)
    assert torch.equal(out, x)
    assert out is not x
@@ -1,127 +0,0 @@
 """Characterization tests for the SDXL options math.
 The SDXL time_ids / text_embeds math lives in
 coreml_suite.core.sdxl as pure builders. The framework adapter
 add_sdxl_model_options lives in models.py; here we just lock the pure
 math.
 """
 import inspect
 import pytest
 import torch
 from coreml_suite.core.sdxl import (
    build_sdxl_text_embeds,
    build_sdxl_time_ids,
    sdxl_model_function_wrapper,
 )
@pytest.fixture(autouse=True)
 def _deterministic_seed():
    torch.manual_seed(0)
 # ---------- build_sdxl_time_ids: base (len 6) -------------------------------
 def test_build_time_ids_base_defaults():
    out = build_sdxl_time_ids({}, {}, is_base=True, is_refiner=False)
    expected = torch.tensor([[768, 768, 0, 0, 768, 768], [768, 768, 0, 0, 768, 768]])
    assert out.shape == (2, 6)
    assert torch.equal(out, expected)
 def test_build_time_ids_base_respects_overrides():
    pos = {"height": 1024, "width": 512, "crop_h": 8, "crop_w": 4,
           "target_height": 1024, "target_width": 1024}
    neg = {"height": 256, "width": 256, "crop_h": 0, "crop_w": 0,
           "target_height": 256, "target_width": 256}
    out = build_sdxl_time_ids(pos, neg, is_base=True, is_refiner=False)
    expected = torch.tensor([[1024, 512, 8, 4, 1024, 1024], [256, 256, 0, 0, 256, 256]])
    assert torch.equal(out, expected)
 # ---------- build_sdxl_time_ids: refiner (len 5) ----------------------------
 def test_build_time_ids_refiner_defaults():
    out = build_sdxl_time_ids({}, {}, is_base=False, is_refiner=True)
    expected = torch.tensor([[768, 768, 0, 0, 6.0], [768, 768, 0, 0, 2.5]])
    assert out.shape == (2, 5)
    assert torch.equal(out, expected)
 def test_build_time_ids_refiner_respects_aesthetic_score():
    pos = {"aesthetic_score": 8.5}
    neg = {"aesthetic_score": 1.5}
    out = build_sdxl_time_ids(pos, neg, is_base=False, is_refiner=True)
    expected = torch.tensor([[768, 768, 0, 0, 8.5], [768, 768, 0, 0, 1.5]])
    assert torch.equal(out, expected)
 # ---------- build_sdxl_time_ids: edge case ----------------------------------
 def test_build_time_ids_neither_base_nor_refiner_returns_len4():
    out = build_sdxl_time_ids({}, {}, is_base=False, is_refiner=False)
    assert out.shape == (2, 4)
 # ---------- build_sdxl_text_embeds ------------------------------------------
 def test_text_embeds_concat_pos_then_neg():
    pos = torch.full((1, 1280), 1.0)
    neg = torch.full((1, 1280), -1.0)
    out = build_sdxl_text_embeds(pos, neg)
    assert out.shape == (2, 1280)
    assert torch.equal(out[0], pos[0])
    assert torch.equal(out[1], neg[0])
 # ---------- sdxl_model_function_wrapper closure -----------------------------
 def test_wrapper_captures_time_ids_text_embeds_refiner_via_closure():
    time_ids = torch.zeros(2, 6)
    text_embeds = torch.zeros(2, 1280)
    wrapper = sdxl_model_function_wrapper(time_ids, text_embeds, refiner=False)
    closure = inspect.getclosurevars(wrapper).nonlocals
    assert closure["time_ids"] is time_ids
    assert closure["text_embeds"] is text_embeds
    assert closure["refiner"] is False
 def test_wrapper_returns_zero_when_context_missing():
    """When c_crossattn is None the wrapper short-circuits to zeros_like(x).
    Locked here because the refactor mustn't change this default."""
    wrapper = sdxl_model_function_wrapper(torch.zeros(2, 6), torch.zeros(2, 1280))
    x = torch.randn(2, 4, 16, 16)
    out = wrapper(
        model_function=lambda *a, **kw: pytest.fail("model_function must not run"),
        params={"input": x, "timestep": torch.zeros(2), "c": {}},
    )
    assert torch.equal(out, torch.zeros_like(x))
 def test_wrapper_refiner_truncates_context_to_g_clip():
    """refiner=True slices c_crossattn[:, :, 768:] before forwarding."""
    captured = {}
    def fake_model(x, t, **c):
        captured["context_shape"] = c["c_crossattn"].shape
        captured["time_ids_shape"] = c["time_ids"].shape
        return x
    wrapper = sdxl_model_function_wrapper(
        torch.zeros(2, 5), torch.zeros(2, 1280), refiner=True
    )
    x = torch.randn(2, 4, 16, 16)
    context = torch.randn(2, 77, 2048)  # 768 + 1280 dims
    wrapper(
        model_function=fake_model,
        params={"input": x, "timestep": torch.zeros(2), "c": {"c_crossattn": context}},
    )
    assert captured["context_shape"] == (2, 77, 1280)
    assert captured["time_ids_shape"] == (2, 5)
@@ -1,34 +1,37 @@
 """Smoke tests for the pure batch-chunking helpers in coreml_suite.core.
 Uses torch.device('cpu') instead of comfy.model_management.get_torch_device
 so Tier 0 runs without ComfyUI.
 """
 import pytest
 import torch
-from coreml_suite.core.controlnet import chunk_control
+from comfy.model_management import get_torch_device
-from coreml_suite.core.inputs import CoreMLInputs
+from coreml_suite.latents import chunk_batch, merge_chunks
-from coreml_suite.core.latents import chunk_batch, merge_chunks
+from coreml_suite.controlnet import chunk_control
-
+from coreml_suite.models import (
-
+    CoreMLInputs,
-CPU = torch.device("cpu")
+)
 from coreml_suite.config import get_model_config
@pytest.fixture
 def expected_inputs():
-    return {
+    expected = {
        "sample": {"shape": (2, 4, 64, 64)},
        "timestep": {"shape": (2,)},
        "timestep_cond": {"shape": (2, 256)},
-        "encoder_hidden_states": {"shape": (2, 77, 768)},
+        "encoder_hidden_states": {"shape": (2, 768, 1, 77)},
        "additional_residual_0": {"shape": (2, 320, 64, 64)},
        "additional_residual_1": {"shape": (2, 640, 32, 32)},
    }
    return expected
@pytest.fixture
 def model_config():
    return get_model_config()
@pytest.mark.parametrize("batch_size", [1, 2, 4, 5, 9])
 def test_batch_chunking(batch_size):
-    latent_image = torch.randn(batch_size, 4, 64, 64).to(CPU)
+    latent_image = torch.randn(batch_size, 4, 64, 64).to(get_torch_device())
    target_shape = (4, 4, 64, 64)
    chunked = chunk_batch(latent_image, target_shape)
@@ -42,7 +45,7 @@ def test_batch_chunking(batch_size):
@pytest.mark.parametrize("batch_size", [1, 2, 4, 5, 9])
 def test_merge_chunks(batch_size):
-    input_tensor = torch.randn(batch_size, 4, 64, 64).to(CPU)
+    input_tensor = torch.randn(batch_size, 4, 64, 64).to(get_torch_device())
    target_shape = (4, 4, 64, 64)
    chunked = chunk_batch(input_tensor, target_shape)
@@ -54,16 +57,16 @@ def test_merge_chunks(batch_size):
@pytest.fixture
 def inputs():
-    x = torch.randn(1, 4, 64, 64).to(CPU)
+    x = torch.randn(1, 4, 64, 64).to(get_torch_device())
-    t = torch.randn([1]).to(CPU)
+    t = torch.randn([1]).to(get_torch_device())
-    c_crossattn = torch.randn(1, 77, 768).to(CPU)
+    c_crossattn = torch.randn(1, 77, 768).to(get_torch_device())
    control = {
        "output": [
-            torch.randn(1, 320, 64, 64).to(CPU),
+            torch.randn(1, 320, 64, 64).to(get_torch_device()),
-            torch.randn(1, 640, 32, 32).to(CPU),
+            torch.randn(1, 640, 32, 32).to(get_torch_device()),
        ],
    }
-    timestep_cond = torch.randn(1, 256).to(CPU)
+    timestep_cond = torch.randn(1, 256).to(get_torch_device())
    return CoreMLInputs(x, t, c_crossattn, control, timestep_cond=timestep_cond)
@@ -83,11 +86,11 @@ def inputs():
 def test_chunking_controlnet(b, target_size, num_chunks):
    cn = {
        "output": [
-            torch.randn(b, 320, 64, 64).to(CPU),
+            torch.randn(b, 320, 64, 64).to(get_torch_device()),
-            torch.randn(b, 640, 32, 32).to(CPU),
+            torch.randn(b, 640, 32, 32).to(get_torch_device()),
        ],
        "middle": [
-            torch.randn(b, 1280, 8, 8).to(CPU),
+            torch.randn(b, 1280, 8, 8).to(get_torch_device()),
        ],
    }
@@ -1,43 +0,0 @@
 """Gate: prove the Tier-0 lane is framework-free.
 In a pure `pytest -m unit` run, none of the banned runtime modules
 (comfy, coremltools, python_coreml_stable_diffusion, folder_paths,
 nodes, comfy_extras, diffusers, diffusionkit) may be in sys.modules
 after collection. If they are, a tests/unit/ file is transitively
 pulling them in and the Tier-0 promise — "runs on Linux with no Mac
 stack" — is broken.
 When other tiers are also collected, framework modules may be imported
 deliberately (e.g. smoke pulls in coremltools), so the check is skipped
 unless the run is purely `-m unit` — Tier-0 purity is only meaningful
 when nothing else is loaded.
 """
 import sys
 import pytest
 BANNED_ROOTS = {
    "comfy",
    "comfy_extras",
    "coremltools",
    "python_coreml_stable_diffusion",
    "folder_paths",
    "nodes",
    "diffusers",
    "diffusionkit",
 }
 def test_no_framework_modules_loaded_by_unit_tier(request):
    markexpr = request.config.option.markexpr
    if markexpr != "unit":
        pytest.skip(
            "purity gate only meaningful in a pure `-m unit` run "
            f"(got markexpr={markexpr!r}); other tiers are expected to "
            "import comfy/coremltools."
        )
    loaded = {name for name in sys.modules if name.split(".")[0] in BANNED_ROOTS}
    assert not loaded, (
        f"Tier-0 leakage: these framework modules are in sys.modules after "
        f"collecting tests/unit/: {sorted(loaded)}. Pure-core promise broken."
    )
Author	SHA1	Message	Date
aszc-dev	d4009e7b69	docs: clarify PYTORCH_ENABLE_MPS_FALLBACK FAQ entry	2025-04-01 23:21:57 +02:00
aszc-dev	b20507e9af	Add basic conversion integration test	2024-06-28 15:52:54 +02:00
aszc-dev	7ea420aef1	Restructure tests directory	2024-06-28 15:52:54 +02:00
aszc-dev	d4bda0e740	Fix set_timestamps for new LCMScheduler implementation	2024-06-28 15:52:54 +02:00
aszc-dev	21ab05fd4f	Change syntax to support older Python versions	2024-06-28 15:52:54 +02:00
Chris Chance	73b13e0d23	Update ModelSamplingDiscreteLCM to Distilled for latest comfyui	2024-06-28 15:52:54 +02:00
Chris Chance	991ab6a40a	Lowered minimum CoreML Size to 256x256	2024-06-28 15:52:54 +02:00
aszc-dev	1320e5cd9c	Add installation using ComfyUI-Manager instructions	2024-06-28 15:52:54 +02:00
aszc-dev	ebdc700177	Add note on SD2.1 to readme	2024-06-28 15:52:54 +02:00
aszc-dev	d752f41b66	Update readme with SDXL info	2024-06-28 15:52:54 +02:00
aszc-dev	ff9a5c91d2	Update converter docs and workflows	2024-06-28 15:52:54 +02:00
aszc-dev	d17a93323f	Remove LCM option from converter for now	2024-06-28 15:52:54 +02:00
aszc-dev	9fb310700e	Converting refiner works	2024-06-28 15:52:54 +02:00
aszc-dev	f95a439d62	Base SDXL conversion works	2024-06-28 15:52:54 +02:00
aszc-dev	b044efe201	Handle SDXL config	2024-06-28 15:52:54 +02:00
aszc-dev	3f666ac0ea	Add Advanced Sampler node	2024-06-28 15:52:54 +02:00
aszc-dev	acecd10aee	Generating SDXL with Core ML Sampler works	2024-06-28 15:52:54 +02:00
aszc-dev	2f6597b8c0	Link to ComfyUI repo	2024-06-28 15:52:54 +02:00
aszc-dev	a03a56a58e	Update REAMDE.md (Conversion and LoRA)	2024-06-28 15:52:54 +02:00
aszc-dev	9b7e5a6cd8	Remove lora.py	2024-06-28 15:52:54 +02:00
aszc-dev	600023382c	Add conversion/lora workflows	2024-06-28 15:52:54 +02:00
aszc-dev	7caf1cea1d	Add peft and omegaconf to requirements	2024-06-28 15:52:54 +02:00
aszc-dev	c6229e5c5f	Load .yaml config if present	2024-06-28 15:52:54 +02:00
aszc-dev	5de5722474	Setting LoRA model weights works	2024-06-28 15:52:54 +02:00
aszc-dev	a3cf825d79	Store lora_params in dict	2024-06-28 15:52:54 +02:00
aszc-dev	75057e4ed2	Add node to load LoRAs	2024-06-28 15:52:54 +02:00
aszc-dev	a1d81faf68	Add logging during conversion	2024-06-28 15:52:54 +02:00
aszc-dev	1ff260fc36	Enable choosing attention implementation during conversion	2024-06-28 15:52:54 +02:00
aszc-dev	83ad02748f	Remove CLIP loader from nodes	2024-06-28 15:52:54 +02:00
aszc-dev	4c9195bbc0	Move lora related code around, remove clip stuff	2024-06-28 15:52:54 +02:00
aszc-dev	7643211d8d	Move load_lora to lora.py	2024-06-28 15:52:54 +02:00
aszc-dev	6be88fee2e	Remove ckpt loading when loading lora clip	2024-06-28 15:52:54 +02:00
aszc-dev	1a82e4b48f	Remove CLIP related code	2024-06-28 15:52:54 +02:00
aszc-dev	7a5d040b61	Basic conversion + LoRA support works	2024-06-28 15:52:54 +02:00
aszc-dev	b4313d731e	Fix category for all Core ML nodes	2024-06-28 15:52:54 +02:00
aszc-dev	9489503cbe	Specify diffusers and coremltools versions in requirements.txt	2024-06-28 15:52:54 +02:00
aszc-dev	edc8e39c83	Add LCM info to readme	2024-06-28 15:52:54 +02:00
aszc-dev	de8915eb6c	Negative optional for LCM	2024-06-28 15:52:54 +02:00
aszc-dev	93ebaf4d5d	Rearrange LCM code	2024-06-28 15:52:54 +02:00
aszc-dev	1bc728d0ea	Core ML Sampler supports LCM	2024-06-28 15:52:54 +02:00
aszc-dev	33829c292f	WIP: LCM Scheduler refactor	2024-06-28 15:52:54 +02:00
aszc-dev	7b1c3c7ba7	Extract lcm sampler from lcm sampling node	2024-06-28 15:52:54 +02:00
aszc-dev	8c9fbacb45	Remove dead code from LCM Sampler	2024-06-28 15:52:54 +02:00
aszc-dev	997c6a78ff	ControlNet works for LCM	2024-06-28 15:52:54 +02:00
aszc-dev	d9be9c13e2	Refactor LCM sampling	2024-06-28 15:52:54 +02:00
aszc-dev	31a6ac6d2f	Download scheduler config from repo	2024-06-28 15:52:54 +02:00
aszc-dev	11772e4e69	Leverage Comfy's mechanisms to enable LCM ControlNet support	2024-06-28 15:52:54 +02:00
aszc-dev	d4f3ed6fa9	Refactor model config	2024-06-28 15:52:54 +02:00
aszc-dev	1d450cca3c	Add CoreMLInputs to handle inputs	2024-06-28 15:52:54 +02:00
aszc-dev	b2102592cd	Refactor CoreMLModelWrapper	2024-06-28 15:52:54 +02:00
aszc-dev	3d7473903b	Wrapped Core ML Model is now diffusion_model attribute of BaseModel	2024-06-28 15:52:54 +02:00
aszc-dev	b12cd83041	Add diffusers to requirements	2024-06-28 15:52:54 +02:00
aszc-dev	ab567e48af	Add newlines	2024-06-28 15:52:54 +02:00
Robert Dean	12f667190f	Update requirements.txt Added overrides decorator	2024-06-28 15:52:54 +02:00
aszc-dev	d612d1ffef	Adjust default values for LCM nodes	2024-06-28 15:52:54 +02:00
aszc-dev	d4666d3615	Remove Simple LCM Sampler	2024-06-28 15:52:54 +02:00
aszc-dev	42c6a66a7c	Add progress bar and preview to LCM	2024-06-28 15:52:54 +02:00
aszc-dev	f4a1eb974b	img2img works	2024-06-28 15:52:54 +02:00
aszc-dev	c09bbeabe2	Add more advanced LCM Sampler	2024-06-28 15:52:54 +02:00
aszc-dev	43f8d330a0	Add support for CN models to LCM	2024-06-28 15:52:54 +02:00
aszc-dev	4e32ca8dbc	Add support for controlnet to LCM converter	2024-06-28 15:52:54 +02:00
aszc-dev	8f639eb2a0	Simplify LCM Sampler	2024-06-28 15:52:54 +02:00
aszc-dev	bfe22d8d06	Fix LCM Sampler	2024-06-28 15:52:54 +02:00
aszc-dev	92080ae196	LCM Converter works	2024-06-28 15:52:54 +02:00
aszc-dev	a638a79f81	WIP: LCM	2024-06-28 15:52:54 +02:00
aszc-dev	d6f7188f7e	Prepare LCM Model Wrapper	2024-06-28 15:52:54 +02:00
aszc-dev	ca715599c1	Fix cn chunking	2024-06-28 15:52:54 +02:00
aszc-dev	ef78f8596f	Fix chunk_inputs	2024-06-28 15:52:54 +02:00
aszc-dev	3d6f8b7dcd	Fix cn chunking	2024-06-28 15:52:54 +02:00
aszc-dev	83f49f0937	Remove the controlnet note in readme	2024-06-28 15:52:53 +02:00
aszc-dev	183d0b2707	Simplify no_control	2024-06-28 15:52:53 +02:00
aszc-dev	ddbeb36d52	Fix controlnet residuals chunking	2024-06-28 15:52:53 +02:00
aszc-dev	2ee81bd41d	Improve chunking and padding	2024-06-28 15:52:53 +02:00
aszc-dev	a1c66249e2	Chunking works for ControlNet	2024-06-28 15:52:53 +02:00
aszc-dev	01fafd70f3	Chunk and pad batches	2024-06-28 15:52:53 +02:00
aszc-dev	447b25c774	Add model adapter for unstable compatibility	2024-06-28 15:52:53 +02:00
aszc-dev	c3038501eb	Rearrange stuff	2024-06-28 15:52:53 +02:00
aszc-dev	b326b3d3b9	Update ControlNet workflow	2024-06-28 15:52:53 +02:00
		`@@ -1 +0,0 @@`
			`e89344e544d4edfbd3ebe9a1c78dadb2729f53549666052b74ac7308f326f4fc`